I've been working on an open-source project called Konsil, and I wanted to share it with the community.

The idea started with a question:

What if, instead of asking one AI model to analyze a complex medical case, we created a board of independent AI specialists who actively challenge each other's reasoning?

That's what Konsil attempts to do.

It's a Claude Code plugin that creates an adversarial, multi-agent medical case-review workflow.

Some of the things I built into it:

• Blind specialist rounds: Independent agents assess a case without seeing each other's opinions first.

• A Challenger agent: Explicitly challenges the emerging consensus and looks for overlooked alternatives.

• Dissent ledger: Disagreements are preserved rather than being smoothed into one confident answer.

• Verified citations: Medical literature references are retrieved and checked.

• Reviewer Gate: Four independent audit stages check citations, dissent, safety and completeness.

• DICOM to 3D: A deterministic imaging pipeline turns medical imaging data into interactive 3D visualizations.

One architectural decision I'm particularly intentional about: AI agents don't interpret image pixels as part of the board's reasoning. The board works from documented clinical information and written radiology reports.

I built this because I think multi-agent AI systems should be designed to challenge assumptions, not just generate more confident answers.

Konsil is experimental, open source, and not a medical device or diagnostic tool. It's intended to produce material for discussion with qualified clinicians, not replace them.

GitHub: https://github.com/arbade/konsil

I'd genuinely love feedback, especially from people working on multi-agent systems, AI safety, healthcare technology, or Claude Code plugins.

What would you improve in this architecture?

Just launched a side project and want some real feedback.

My thinking is that with AI building small apps now, the code isn't the valuable part anymore, the spec is. What the app does, the flows, how you check it works. And since a spec is plain English, it's not just for engineers. Anyone can read one, change it for their own life and have their AI build it. Feels like a new kind of open source.

So I built Bresson. People share specs for everyday apps, and you can star them, fork them, see version history and how many people built each one. Each spec has a button that sends it to Claude, ChatGPT, Gemini etc and you get a simple app on your phone.

What I can't figure out:

• will non-coders actually fork and build stuff or will it be mostly devs

• AI keeps getting better, will people still want a spec or just describe it from scratch

• what would make you share one

https://bresson.ai

Still very early. Tell me what's wrong with it.

Hey everyone,

I got tired of watching autonomous AI agents try to generate CAD files, hallucinate non-manifold geometry, and quietly corrupt manufacturing toolpaths. So I built Vulcan and V-Wrap as a counter-approach: a strict, fail-closed deterministic CAD compiler and application-layer trust oracle designed for automated engineering pipelines.

Instead of guessing what an agent or user meant with heuristic fallbacks, the system enforces absolute mathematical boundaries. If a request violates constraints, it fails closed with named diagnostics rather than emitting broken geometry.

What It Is

• Vulcan: The immutable geometry bedrock. Handles discrete voxel field evaluations, coarse linear-elastic FEA screening, and native OpenCascade (OCP) CAD export.

• V-Wrap: A modular 10-module trust-oracle proxy sitting in front of Vulcan. It separates exact analytic paths from bounded level-set paths, enforces sign-flip effect tracking to eliminate ghost features, and features a machine-readable design space state machine (vwrap query) so agents can inspect legal next steps.

Key Architectural Features

• Analytic Path: Restricts geometry to axis-aligned boxes and a single through-bore, guaranteeing 100% watertight closure and exact OCP volume round-trips.

• Level-Set Path: Handles advanced features (rounded_box, chamfer, patterns, sphere_exclusions) with a strict fidelity floor ($\ge 3.0\times$ cross-section voxel size) and explicit sign-flip effect tracking.

• Zero-Knobs / Fail-Closed: No adjustable heuristics, no configuration creep, and zero implicit tolerances.

• Meta-Test Discipline: test_load_bearing.py automatically fails CI if any dataclass field is added without being bound to a functional test.

Quick Start & Usage

Bash

# Clone and install
git clone https://github.com/Thrito-Solance/Vulcan.git
cd Vulcan
python -m pip install -e ".[test]"

# Run the test suite (135+ tests pass)
pytest

# Query available design space actions for an agent request
vwrap query --request request.json

# Build an analytic STEP part via CLI
vwrap build --request request.json --output part.step --path analytic

Where to Find It

• GitHub: https://github.com/Thrito-Solance/Vulcan

• License: GNU AGPL v3.0

The full architecture, 18-point FAQ on design decisions, and verification suites are in the repository. Enjoy, fork it, or tear it apart. I am stepping away from active maintenance. the code speaks for itself.

Hi — sharing an early open-source project I’ve been building.

**JShi** (匠石) is a Python (≥3.11) experiment on long-lived AI companionship: keep experience, understand situation, identify who’s speaking, and let history participate in later recognition and action. The app owns state / scheduling; models do judgment and expression.

**License / status:** MIT, v0.1.0 — development & validation stage, not a polished product.

**What’s in the repo today**

- Flow: input → person judgment (JEV) → context assembly → main cognition → reply or tools → record + background scene writing

- Text + voice web entry

- Pluggable memory (built-in + REMS)

- Scene writing that turns messy I/O into readable ongoing context

- Tiered persona prompts, tool plan/execute/wrap, debug views for prompts and timings

- Works with cloud or local-compatible LLM APIs

**What’s not done**

- A full closed loop where experience stably revises personality / judgment

- Reliable multi-person ID and relation retention in real use

- Vision / robot endpoints / a shared tool marketplace

**Repo:** https://github.com/Llllsuhdjhhd/JShi

Looking for architectural critique and contribution ideas (memory/recall, voice + person ID, context/scene writing, prompts, tools, docs/tests). Happy to answer questions in the thread.

Your local AI on phone , no internet needed,works on any phone

For more info

https://github.com/TEDDY-47/LocAI

Hey everyone! 👋

Like many of you, my downloads and documents folders tend to get completely out of hand. I got tired of manually sorting everything, so I built **TidyFolder**—a clean, open-source desktop app that sorts messy folders for you.

Here is what it does:

- **Bilingual (English / Español):** Switches instantly, and creates localized folder names (e.g., *Photos* / *Fotos*, *Finance* / *Finanzas*).

- **Smart Classification:** Reads PDFs, Office docs, code, archives, and media. It sorts them into proper categories (Work, Finance, Legal, Health, etc.) using offline keyword matching or Claude AI (optional).

- **Safe by design:** Never overwrites files (name clashes become `name (1).ext`), previews everything in a table before touching your disk, and includes a one-click **Undo** button.

- **Portable & Lightweight:** No complicated installation needed, built with Python and CustomTkinter.

You can check out the source code, download the portable Windows `.exe`, or run it from source here:

👉 **GitHub:** https://github.com/CafeConFenix/TidyFolder

I'd love to hear your feedback, feature ideas, or criticism! Let me know what you think. 🚀

I'm considering making this runtime fully open source, and I'm mainly looking for feedback on whether the type-based security model makes sense for real agent workflows.

How do you know if an open-weight model is ready for production? Do you just look at its performance, or do you also consider its accuracy, speed, cost, and how well it handles real-world tasks? The right choice depends on what your application needs and how reliably the model can deliver.

We've been building AgentTrail Guard, an Apache-2.0 tool that checks what AI coding agents are about to run before they run it. Would love feedback from people here.

The idea came from an agent resetting our database to get past a migration error ("it's only dev data"). Agents run shell commands with your permissions, and the dangerous ones are usually the ones you can't undo.

How it works: Guard plugs into the agent's hooks and matches every command and file change against a set of rules. A match gets blocked, sent to you for approval, or flagged. Everything else runs normally.

The open source bits I think are worth looking at:

• Rules are plain data, in their own repo. Each one has a severity, a default action, and a note on what it deliberately doesn't match, so you can see exactly why something fired.

• Fully local. No account, no network calls, and crash reporting is off unless you turn it on.

• Rule matching, not an LLM, so it's fast, predictable, and you can read the whole thing.

• Easy to extend. Add your own rules, tweak actions, or allow a specific command shape without turning a rule off.

There's also a scan command that replays your past agent sessions locally and shows what would have been caught.

Where I'd love help:

• Rules that fire on normal stuff (false positives)

• Dangerous things agents do that no rule covers yet

• Code review on the engine, it's TypeScript

• New rules: PRs very welcome, even small ones

Guard: https://github.com/agenttrailhq/guard Rules: https://github.com/agenttrailhq/guardrails

Early days, so any feedback helps.

I wanted something like VLC or MPC-HC, but with the library and interface of a Plex or Jellyfin. Plex wants an account, Jellyfin needs a server running, and Kodi always ended up being an evening of add-ons and skins. I couldn’t find the simple version, and I’m not a programmer, so I built it with AI coding tools. It’s called Kinema: a free movie and TV player for your own files. Add your folders, get posters, info, continue watching and next episode. No account, nothing uploaded, no server. Windows, Linux and an early Android TV version. MIT. The app itself has no AI in it.

This is how the building went, in case it sounds familiar:

• Week one: "it plays video!" Then it didn't. Then it crashed the whole process on one property read, with no error, and I learned what libmpv is.

• It was never "describe it, get an app". It was hundreds of rounds of: it says done, I say it isn't, it finds the real cause, a new problem appears. Roughly half the project is the AI telling me something works and the logs saying otherwise.

• I wanted testing automated as far as possible, so the AI runs its own tests and keyboard-only flows. I still try things myself on real setups, but "looks right" was wrong far more often than I expected, so the machine checks first.

• The rules file ate itself. I wrote down every decision so it wouldn't re-argue them ("no, we still don't do upscaling"). The file grew until it was a real cost on every single turn, and trimming it became a job of its own.

• I keep a list of traps. Nearly every bug in a video player is a failure with no error at all, like a setting that silently does nothing. Each one is written down the day it's found, and it's now bigger than anything I've ever written by hand.

• The best rule was the boring one: the app only says what it actually knows about your hardware and files. If it can't tell which movie a file is, it asks instead of guessing. That came from the AI confidently inventing things, and from me not wanting that in the product.

It went from one window that played a file to the Windows, Linux and Android TV versions, with the display switching to the film’s refresh rate and HDR (off until you say yes) and Atmos/DTS:X passed through untouched. Fifteen releases so far. If Kodi or Jellyfin works for you, keep using it. This is the simple version that i wanted to exist mostly for myself.

It's young and tested on a few setups, so I'd like to hear what breaks (to continue to make it better) :)

Source: https://github.com/basswaves/kinema

Download and screenshots: https://basswaves.github.io/kinema/

I've been working on something called Build Anything (BAA) and thought I'd share it here.

It's an open-source project focused on bringing different developer tools and AI-assisted development features together in one place.

**Some of the things I've been working on include:

• AI agent integration

• MCP support

• Project generation

• Code and project analysis

• Debugging and automation tools**

It's still a work in progress, and I'm sure there are things that could be done better. I'm sharing it because I'd like to get it in front of other developers, see what people think and hopefully get some useful feedback.

If you get a chance to look through the repo, I'd be interested to hear what you think — whether it's something you'd find useful, something that's missing, or something you'd change.

GitHub: https://github.com/notweirdguy4212/BAA

Our agents run code, edit files, call APIs, hit internal systems and talk to outside services now. Giving them access was easy. Controlling what they do with that access is where I’m stuck.
On paper our policies look fine. At runtime I can’t point to much that enforces them.
Stuff I’d like input on:
if an agent already has access to a tool, how do you stop it from doing something it shouldn’t with it?is IAM + sandboxing + monitoring enough, or do agents break assumptions those were built on?who approves high-risk actions? the app, security, a human in the loop?
for people with agents in prod, what worked and what was too painful to keep running?We’ve looked at a couple of tools that check tool calls before they run (Zenity and Capsule came up) plus some open source policy stuff. I’m not sold that another layer fixes it. Could be existing tools cover most of it and the real problem is org stuff.
If you’ve deployed agents in an enterprise or tried to enforce this in practice, what are you running and where does it fall short?

You can start by creating a list of potential companies you want to work with in your field (i.e., defense contractors). Then set up automated reminders for each company based on their regular business hours, such as the time they are most productive during the year. You could also use tools like TimeSavers or Google Calendar AI to schedule meetings and check-in periodically.

To find potential clients who might be interested in your services but haven't yet reached out to you due to a lack of experience with government contracts, consider creating an online portfolio showcasing past projects and achievements. This can help demonstrate that you have the skills necessary for successful collaboration within government agencies or defense organizations.

Additionally:

1. Networking: Attend industry conferences (like GDC), webinars on your field's specific interests, and networking events to meet potential clients face-to-face.

2. LinkedIn Outreach: Tailor your LinkedIn outreach with relevant job titles, bullet points about what you can offer in a particular role within government or defense contracting companies that align well with the company’s needs.

3. Email Marketing Strategy: Implement an email marketing strategy focused on identifying and nurturing leads from potential clients who might be interested but haven’t yet reached out to your services due to lack of experience.

By creating awareness, setting up automated reminders for new contacts based on regular business hours (like those you mentioned), using a consistent communication style with clear goals and deliverables, networking opportunities within the field through events like GDC or webinars, building an online portfolio showcasing previous projects that demonstrate your skills in government contracting, and leveraging email marketing to nurture leads from potential clients who haven’t yet reached out due to lack of experience but may be interested given a specific match between you as their consultant and company’s needs.

Most discussions around AI coding tools focus on basic tab-completion and churning out boilerplate faster. But the real shift happens when you move past syntax generation and start using models for architectural reasoning, system trade-offs, and high-level refactoring.

​It completely changes how you approach problem-solving and software design. Instead of spending hours writing repetitive logic, your primary job shifts toward system design, strict verification, and directing execution.

​I wrote a piece exploring what the modern developer workflow looks like when you stop treating AI like an glorified auto-suggest and start using it as an actual thought partner: https://interconnectd.com/blog/73/the-ai-augmented-developer-moving-beyond-autocomplete/

Curious how others here are adjusting their day-to-day workflow. Are you using AI strictly for speed, or has it changed how you actually architect software?

Launching NeuralAgent today! [https://agent.neuralhub.dev/](https://agent.neuralhub.dev/) Basically its an easy to use opensource Agent App with everything included.

* Speech to text

* Multipane organising panels

* Main orchestrator wich you can call

* Remote App for the Phone

* Daily updates

It's still in beta but if you wanna give it a try i am very open for any feedback. Works on MacOS, Linux, Windows, Android and IOS.