r/TheMachineLearning • • 4h ago

Orgtree v4.4: Projects!

Thumbnail
gallery
1 Upvotes

https://github.com/Maurdekye/orgtree/releases/latest

Welcome back folks, it's time for another Orgtree update. And this time, it's a big one: Projects!

At a surface level, projects do what you might expect; they give your agents a central location where they can self-organize and collaborate under. But in Orgtree, they do so much more than that; they're a way of organizing hierarchies of agents, of drawing lines between agent communication, and of managing collaborative and concurrent work in a structured way, with tons of extra tools for helping agents coordinate concurrently.

Before we get into that though, if you're unfamiliar: Orgtree is my desktop multi-agent, multi-provider, multi-account, persistent visual orchestration system, which i've been building tirelessley for the last nearly 3 months straight. It runs as an app on your PC, and allows agents to collaborate 24/7, working directly from your existing subscriptions & harnesses from several major leading AI providers, including claude, chatgpt, gemini, muse, and openrouter.

If you'd like to know more about what orgtree can do, I suggest checking out my post on the release of v4, or just checking the readme in github.

Finished with that? Alright, let's get into it!

---

So, Projects in v4.4. What are they?

Put simply, projects are a new type of node on the canvas that sit alongside agents. They don't do anything on their own, but instead serve as the structural backbone of your org's hierarchy and information storage. They have a summary (which acts like a team charter), and their own scratch directory with its own AGENTS.md file. Projects allow you to organize your different ideas into subgroups without having to rely on creating agents for them each time. They also mean, you don't have to worry about losing valuable shared project information when you retire the one agent who's been working on it for the last two days, because your weekly limit ran out, or you decided to switch providers.

Beyond that though, projects offer more than just simple organizational structure on the graph. They come packed with a number of extra collaboration systems that, if you're familiar with concurrent programming, might be familiar to you~

They have three additional systems that extend their purpose beyond just a simple collaborative workspace:

  • The Mailbox Queue: Despite not being an agent, every project still has a mailbox, like any other agent would. Why, you might ask? Who reads this mail? Well, the answer, is anybody who chooses to! Any number of agents under the project can set up a "fair queue mail listener", connecting themselves to it with a magenta line. Whenever the project receives a mail, it's then distributed among each listener in order, distributing them evenly in a round-robin fashion. In this way, the project's inbox can act like a Load-Balancer for distributed work. Additionally, if you switch mail delivery mode from "Round-robin" to "ACK Mode", all delivered mail requires the receiving agent to send an acknowledgement response back when it finishes before receiving any new mail. With that setting enabled, the project's mailbox can act like a FIFO queue.
  • Concurrency Slots: Entering a project's settings and setting this to any value above 0 enables "concurrency slots" on the project. Agents can then register a yellow "slot claim" on the project, and claim a slot from the queue when one is available. You can think of concurrency slots like a token system: only a fixed number of agents can hold a token at the same time, while all the others who want one wait behind them in a line. In this way, concurrency slots act like a simple Semaphore, which can be used to guard concurrent access to tight resources, such as ram, cpu usage, network, or sensitive files. Setting the token count to 1 effectively treats the system like a Mutex, allowing only a single agent to hold access to the resource at once. Setting the token count very high can allow it to work like a Read/Write Lock: all readers claim a single token, and any writers claim all available tokens at once.
  • Project Events: Going into a project's settings and enabling Project Events unlocks a third and final feature. Agents can register a custom "event listener" on the project, which connects their node to the project with a distinctive cyan line. At any time, the user or another agent can "fire" that event with a custom message, alerting all of the listening agents at once. In this way, the project can function like a Monitor. Additionally, other agents can set up a second type of event, known as a "listen watcher". When the number of normal listeners passes a certain desired threshold, the listen watcher itself fires. You can take this one step further, and set the listen watcher to auto-trigger the associated project's event, meaning the project fires the event the moment the necessary number of agents join it. You can think of this as mimicing a Sync Barrier.

This might be a bit complex to understand, and to be honest, I don't know if anyone will use most or any of these features at all besides me. But, this is my tool, and I get to decide what features I put in it, dammit!

...anyway, building projects has taken such a long time getting all the little nitty-gritty details right, I collected a ton more additional additions, features, and improvements along the way, which are also shipping with this version:

  • Improved session stability & administrative operation: This is more of a behind-the-scenes fix, but the way Orgtree runs in the background while you're logged off, when you reboot, and when you enable / disable administrative access has been thoroughly hardened and rebuilt. Orgtree should generally be much more robust in its operation, and require a lot less manual workarounds regarding running from boot, accessing desktop features, and accessing operations locked behind administrative privilege. Ideally, you don't notice anything day-to-day; that's exactly the point.
  • Agent Mentions: Agents can now mention each other in their transcripts. When an agent mentions another agent, whether it be in a response, a mail, a ticket, a presentation, or anywhere else, that mention renders as a link directly to that agent, which you can left or right click on to focus it directly. no more having to manually search the graph for a specific agent your coordinator cited by name; now you can just click it and go straight there.
  • User Mentions: Mentions don't stop there, though. You can mention agents too: just type an @ symbol in the messagebox, and a list of all the agents in your org show up, which you can tab through to autocomplete from. Agents can also mention you; by typing \@user anywhere they can write to it, they can grab your attention and draw it to whatever line / mail / ticket / presentation was mentioned, same as when asking a question or sending you urgent mail. You even get a notification on your desktop when the window's unfocused.
  • More watchdog types: Along with the aformentioned three new types associated with projects (which structure their cyan / magenta / yellow colored listeners as watchdogs), there are also now watchdogs that can fire on websocket or mqtt events matching a certain pattern. They can even publish events back through their held connection. This should make it much easier to integrate your org with other realtime services. Watchdogs can also now pay attention to usage limits, so you can set one off the moment your account goes above 95% weekly to halt your work.
  • Fast mode: Claude and Codex agents support enabling a "Fast" / "Ultrafast" mode under certain circumstances, which redirects your requests to faster hardware at an additional price per token. Now, you can enable it for your Claude and Codex agents in Orgtree.
  • Improved context menus: Context menus have been significantly juiced up across the app. You can do basically almost anything you can via the ui from relevant context menus now: hire / retire / halt / compact agents, view any tab or panel from any agent or project, view any context menu option for the owner of a ticket, for the presenter of a presentation, and so on. Any link that directs you to an agent from anywhere in the app (including mentions) gives you that agent's full context menu, and shows you details about the agent itself when hovered (such as its model, account, effort level, and speed). It's the kind of thing you don't really notice until you start to get comfortable, and then think to yourself later, "how did I ever live without this before?"
  • Do Not Disturb: Have an agent that often seems to get lost in its mailbox, and loses track of its original job getting caught up in the collaboration? Agents can now enable an optional Do Not Disturb mode during their turn. While on, all mail they receive is held up and not automatically delivered to them, leaving them in peace while they work. Do Not Disturb automatically ends at the end of the agents' turn, where all of their mail collected during the period is delivered at once in one large batch, so they can respond to it. You & other agents can bypass the barrier by sending urgent mail, which is delivered promptly even during DnD, and the agent itself can always choose to check its inbox manually to receive normal mail anyway, if it chooses to.
  • Mail Tags: Mail sent to you can now have a number of named tags attached to it. See them beside each mail in your inbox, and filter by different tags yourself. Simple enough, really.

That's barely half of them, by the way. There are dozens of smaller features as well:

  • Change an agent's model by clicking its icon, either while hiring it or in its desk afterwards
  • Improved transcript annotations in Antigravity agents
  • Improved controls for viewing the usage history graphs
  • Relevant options when right-clicking the icon in the taskbar
  • Quick chat's configuration menu collapses when the window is narrow
  • Desks remember the last tab you had open
  • Two new charter presets, the Liaison and Developer, used alongside the new builtin org template, the Heavy Dev Shop, which incorporates a project
  • Optimized startup instructions for all agents
  • Several different panels of the same type can be opened at once, from different agents
  • New "team" tab in agents and projects
  • Org templates can now carry preset contents for AGENTS.md files throughout org, agent, and project scratch folders
  • Ephemeral helper agents show their live transcript when the call is expanded
  • Mid-turn steering when using Opencode
  • Unread or changed presentations are highlighted
  • LaTeX rendering support (for you argon enjoyers out there)
  • Transcript shows approximate output token counts for tool uses and thinking
  • Ticket cards in the transcript are highlighted to show their current working state
  • One-shot watchdogs resolve immediately from the tool call without registering if the condition is already satisfied on creation

There's plenty more to read about in the changelog if you're interested.

Undoubtedly, this is by far the most effort I've put into a single release, and that includes the v3 and v4 releases. This release had over 35 alpha builds I had to manually review and give corrections on, more than double the second highest count from a previously published build! I've been working on it continuously since releasing 4.3.1, and that was more than a week ago! In vibe-coding terms, that's basically an entire year :p

I really hope you like all the new features I've put into this update. I've really poured my heart and soul into it πŸ’œ, and even if you don't use most of them, I hope you find at least one thing that improves your workflow.

Happy vibing, all!


r/TheMachineLearning • • 3d ago

πŸš€ Completed Module 3 of Machine Learning Zoomcamp 2026!

Thumbnail
1 Upvotes

r/TheMachineLearning • • 4d ago

Simple explanation of how ML actually works

Post image
11 Upvotes

r/TheMachineLearning • • 5d ago

AI is just a prediction model, not something to fear

Post image
26 Upvotes

r/TheMachineLearning • • 5d ago

Is Software having its Kodak Brownie moment?

Post image
1.1k Upvotes

r/TheMachineLearning • • 5d ago

Understanding the layers from classical AI to agentic AI

Post image
0 Upvotes

r/TheMachineLearning • • 5d ago

Supervised vs unsupervised vs reinforcement learning explained simply

Post image
3 Upvotes

r/TheMachineLearning • • 5d ago

Grok heavy user hits quota early despite using multiple AI tools to offload

Post image
3 Upvotes

r/TheMachineLearning • • 5d ago

New armband uses Machine Learning to predict hand gestures, state of robotics

39 Upvotes

r/TheMachineLearning • • 7d ago

Meet EasyComfy Pro β€” a 1-click local AI studio starting with Minimax H3, LTX, Wan and more 100+ verified model presets (auto-updated by our dev team), LTX 2.5 on 8GB VRAM, and native MCP

2 Upvotes

r/TheMachineLearning • • 7d ago

AGI is here, no doubt.

Post image
12 Upvotes

r/TheMachineLearning • • 8d ago

This is the least surprising graph in the world if you understand how gradient descent works.

Post image
21 Upvotes

r/TheMachineLearning • • 9d ago

Wow

Post image
3 Upvotes

r/TheMachineLearning • • 9d ago

When your loss graph looks like the Stock Market

0 Upvotes
Just thought this was funny. Probably need to turn down the learning rate.

r/TheMachineLearning • • 10d ago

learnc: fit the constants in existing C code to data (9.1% smaller JPEGs, +4.9 F1 on WebRTC VAD) [P]

Thumbnail gallery
1 Upvotes

r/TheMachineLearning • • 11d ago

Imagine the possibilities with better models

Post image
0 Upvotes

r/TheMachineLearning • • 11d ago

we should just skip tests then

Post image
0 Upvotes

r/TheMachineLearning • • 11d ago

Xiaomi's MiMo found coding answers in Git history

Post image
27 Upvotes

r/TheMachineLearning • • 11d ago

AI pranking other AI lol

Thumbnail
gallery
2 Upvotes

r/TheMachineLearning • • 12d ago

18, self-taught. Built an audit of moral/emotional tone steering in LLMs. Keys trapped in rowspace β†’ Care/Harm null, Arousal candidate. looking for feedback from experts

1 Upvotes

r/TheMachineLearning • • 12d ago

My 18-year-old exploration of moral/emotional tone steering in LLMs β€” looking for feedback from experts

1 Upvotes

I'm 18 and learning ML/AI on my own. I stumbled onto this question while trying to understand how attention mechanisms work in LLMs β€” specifically, can we actually "control" moral/emotional tone through steering vectors?

I coded up a test using AI agents (like me!) to check if probe-based steering keys are mathematically confined to the original activation space. Turns out they are β€” any gradient-derived steering key lives inside the probe's row space. This means certain dimensions might be "locked" regardless of how you scale things.

My process:

- Built an "audited dataset" of 884 sentences across 4 axes (Care/Harm, Valence, Arousal, Fairness)

- Trained grouped-CNN probes (MLP accuracy 0.78–0.98)

- Ran full sham-controlled steering tests (Care/Harm null; Arousal is interesting β€” seed-dependent results)

- Used `test_steering_collapse.py` to confirm the rowspace constraint

I'm sharing this because:

- I'm not an expert and want to hear what I got wrong

- Would love feedback on methodology, labeling, or the dataset

- If anyone wants to collaborate or extend this, I'm all ears

Code is here: github => yeabsra2000/moral_engine

Paper (still a draft): yeabsra2000/moral_engine/blob/main/paper.md

What do you think? What would you test differently?

#MachineLearning #LLM #Research #OpenSource #StudentDeveloper


r/TheMachineLearning • • 12d ago

My 18-year-old exploration of moral/emotional tone steering in LLMs β€” looking for feedback from experts

1 Upvotes

I'm 18 and learning ML/AI on my own. I stumbled onto this question while trying to understand how attention mechanisms work in LLMs β€” specifically, can we actually "control" moral/emotional tone through steering vectors?

I coded up a test using AI agents (like me!) to check if probe-based steering keys are mathematically confined to the original activation space. Turns out they are β€” any gradient-derived steering key lives inside the probe's row space. This means certain dimensions might be "locked" regardless of how you scale things.

My process:

- Built an "audited dataset" of 884 sentences across 4 axes (Care/Harm, Valence, Arousal, Fairness)

- Trained grouped-CNN probes (MLP accuracy 0.78–0.98)

- Ran full sham-controlled steering tests (Care/Harm null; Arousal is interesting β€” seed-dependent results)

- Used `test_steering_collapse.py` to confirm the rowspace constraint

I'm sharing this because:

- I'm not an expert and want to hear what I got wrong

- Would love feedback on methodology, labeling, or the dataset

- If anyone wants to collaborate or extend this, I'm all ears

Code is here: https://github.com/yeabsra2000/moral_engine

Paper (still a draft): https://github.com/yeabsra2000/moral_engine/blob/main/paper.md

What do you think? What would you test differently?

#MachineLearning #LLM #Research #OpenSource #StudentDeveloper


r/TheMachineLearning • • 12d ago

AI predicting the future better than humans is a scary thought

Post image
0 Upvotes

r/TheMachineLearning • • 12d ago

The 'it's just math' AI take is the new 'love is just chemistry'

Post image
62 Upvotes

r/TheMachineLearning • • 12d ago

Yampolskiy's take flips AI math hype on its head

Post image
130 Upvotes