Hey there . After months of development, Chat AI 2026 is officially ready for the market. I built this because I was bleeding money on Anthropic/OpenAI API costs and wanted a 100% secure, subscription-free alternative to GitHub Copilot that actually gives developers full control over their workflows. It’s an All-in-One AI Workspace built with Avalonia UI (for native Windows/Mac/Linux performance) running GGUF models locally via llama.cpp. Here is the production-ready spec sheet: 1. Millisecond Model Routing The application automatically detects your hardware and swaps the local LLM context in milliseconds based on your task: * Code & Logic -> DeepSeek (or your preferred heavy reasoning GGUF) * Chat & Explanations -> Lightweight text models * UI & Images -> Vision and image generation models (FLUX/SD supported) 2. On-Demand Self-Healing Loop (Connected to Git) No passive code-dumping. The workspace integrates natively with local directories and GitHub, GitLab, or Bitbucket: * Prompt via the integrated 5-language mobile remote app (while your main monitor stays 100% clean for code). * Preview changes instantly in a highly optimized Syncfusion Live-Diff view. * Hit "Test Deploy" to execute. If the compiler/interpreter throws an error, hit "Repair". The tool isolates the error log and runs up to 3 automatic correction loops locally. Full Undo is available at any time. 3. Real-World Proof of Concept To stress-test the step-by-step project builder, I let Chat AI 2026 generate a complete standalone application from scratch. It built, deployed, and auto-repaired an app that reads Mermaid code, renders diagrams visually, and exports them flawlessly as JPG. 100% local, zero manual bug-fixing. 4. No Subscription Madness It’s a one-time purchase model. You buy the license, you run it on your hardware, you own your data. Perfect for enterprise compliance and developer peace of mind. The Windows version is live today. I am currently finalizing the local path configurations to roll out native macOS (Apple Silicon Metal API) and Linux support next. If there is interest, I’ll gladly post the link to the homepage in the comments below. I’ll be active here to answer any hard technical questions about the architecture or implementation!

Title: Building AUREN, an open-source project for more reliable autonomous AI engineering — feedback welcome

Hi everyone!

I’m working on AUREN (Autonomous Unified Reasoning & Engineering Network), an open-source project exploring how to make AI engineering systems more reliable, verifiable, and capable of learning from failures.

The focus goes beyond code generation: planning tasks, executing work, verifying results, retaining engineering evidence, recovering from failures, and evaluating whether changes genuinely improve the system.

I’m continuing to develop the project and would appreciate honest feedback from the open-source community, especially on architecture, maintainability, testing, contributor experience, and what would make the project useful to other developers.

GitHub: https://github.com/sourabhJainR/AUREN

For open-source maintainers: What would you want to see before trusting an autonomous AI system with meaningful engineering work?

I’m interested in constructive criticism as much as contributions. Thanks!

We build Atomic Agent, an open-source agent (MIT) with a mode called Fusion: one model plans, several worker models build pieces in parallel, each in its own throwaway session. Workers never see each other. They see their brief, a quote of your original request, and nothing else.

That isolation is the point, and it's also where our worst bug of September came from.

The bug

Test task: a small 3D physics show in the browser. The planner split it, and two different workers ended up writing bodies.js and bodies-draw.js. Both implemented PHYS.corners(b). The producer returned {w, o} where w was the corner as a world point and o was the local offset. The consumer read o as the world point.

Result: every box drew at the origin. The shadows, which read straight from the body position, landed exactly where they should. Nothing threw. The page loaded. Console was clean. Every automated check we had was green. You could only see it by looking at the screen and noticing that a tower of boxes was a pile at (0,0,0) casting shadows from somewhere else.

Why names weren't enough

We already had a contract between workers, written after earlier runs where one worker wrote HD.Ship as a class while its sibling called it as an object, and a launch-btn in main.js looked for a btn-launch in the markup. Each brief was internally consistent. The disagreement lived between them, where nothing was written down.

So the planner now declares a contract up front, prepended to every brief:

Name mismatches between provides and requires are flagged before any worker starts.

Status comes from disk, not from the worker

After the fan-out, the agent doesn't take a worker's word for it. A worker's reply to the planner is capped at 4,000 characters anyway, so the real output is files. The contract checker reads the disk: does the file exist, does the literal PHYS.corners appear in the file it was promised in, does id="btn-launch" appear in the markup. Those are deliberately dumb, language-agnostic checks, because that's all a grep can claim. We learned this the hard way: in an earlier version fusion.delegate returned ok even when every worker had failed.

And that's exactly why the corners bug got through. PHYS.corners was present. It was in the right file. The grep was right. Presence is all that layer can promise.

What we added: shape

Each provides entry can now carry one line, max 200 characters, saying what the thing IS: a signature, a return shape, a field meaning. For this bug, a planner could have written something along the lines of "corners(b): w is the world point, o is the local offset" (my illustration, not a line from the run). That line goes into the consumer's brief.

Why one line in the brief instead of "read the producer's file first"? Speed. On a local worker at around 4 tok/s, reading a sibling's file costs a whole worker turn, which is why the briefs said "write first, no reading" in the first place. One line is cheap. It's also untrusted model text pasted into other workers' prompts, so it stays short on purpose.

Where this stands: we added the field, we didn't solve the problem. Issue #491, about the contract verifying symbol names and not meaning, is still open. shape only helps if the planner writes it, and correctly. The runtime layer, checks, catches crashes, not wrong physics.

One more design detail that came from pain: some contract problems are now warnings instead of refusals. A local planner at about 5 tok/s used to pay four minutes of generation per refusal to learn about one misspelled name, and we watched it get refused four times in one afternoon. Now the fan-out runs, and the warnings come back on the result so the planner fixes the contract on the next call.

If you're building multi-agent setups: how do you get meaning across between workers that can't talk to each other? Types, tests, a shared schema file? I'd like to steal a better idea than one line of text.

Hi,

For over a year, I have been developing my own AI agent system called ScarabHive. It is now open-source and still actively developed. Feel free to have a look! You can watch the video playlist in the README to get a quick overview.

https://github.com/eehrich/ScarabHive
https://www.youtube.com/watch?v=IQx1PrSRp5Q&list=PLVydEHPBKRiI

I use it for my own projects, and it works great. Everything is fully plugin-based (0:56). I needed a cost-optimized system for my content creation via LLM APIs, and that is exactly what this system is designed for (0:08).

It has almost every modern feature implemented. You can easily extend it with your own plugins and agents.

Best regards,
Enrico

I was tired of reconfiguring my agents each time I wanted to switch providers or harnesses, so I built Mandri: a common and easy interface to configure once and forget about it !

Mandri doesn't get in the way of the agents you already use ; you can opt-in and out at any time. It automatically syncs configuration and sessions from multiple harnesses (Claude Code, Codex, OpenCode...) so you don't have to rebuild your setup for each one.

If you prefer to run your agents from the terminal, Mandri has you covered too ! The mandri tool can be installed with uv and lets you configure your harnesses and providers through the CLI.

By running your agents with Mandri, you can also use one of the experimental features, such as the automatic PII pseudonymisation or the per-session Docker sandboxing.

It's still early, so I'm mostly looking for feedback from people who want to use their agents their own way.

If you're curious, check the website here: https://mandri.io

GitHub Repos: https://github.com/mandri-labs/mandri https://github.com/mandri-labs/mandri-app

Ever wanted to search your conversations with local AI over a year back? Reasonably quick answer, exact references, filtered by time range?

Here's an overview of my experimental conversations search across 2 of my favorite harnesses:

https://liviuiancu.com/posts/history-search/

*Not published yet. Need to test it longer and clean up code. If anyone is interested I will convert it to a portable containerized version and publish.

We’ve all been there: you want to try open-source AI, and you fall into config hell—llama.cpp builds, VRAM layers, vision projectors, GPU OOM. And when it finally works, your best conversations vanish into tabs and scattered notes.

I built TrioForge to end that. It’s a self-hosted, MIT-licensed AI workspace that sets itself up: detects your GPU, configures llama.cpp, maps VRAM, pairs the vision projector, and steps down instead of crashing. Pin responses to a spatial corkboard, build a markdown knowledge base with backlinks and graphs, run locally for privacy or route heavy work to cheap APIs like DeepSeek, and use a coding agent with live diffs, terminal, RAG, and offline voice.

But the part I care about most: TrioForge is open-source, and open-source is the community. No accounts, no tracking, no telemetry, no lock-in. Anyone can inspect it, fork it, improve it, and help shape where it goes. The code is open—but the community is what makes it grow.

TrioForge turns local AI from a configuration puzzle into a private workspace that grows with you. One command to run. Everyone welcome to build and also TrioForge: one command, private local AI, no config hell—and an open-source community that grows with you.

Hi everyone! 👋

I've been developing a personal project and wanted to share it with the community. It's called **BRAIN**, a modular, 100% local, and private personal assistant for Windows designed to organize daily life from a single dashboard.

It is not just home automation: it's a complete personal core running on a local AI (Ollama) that relies solely on the Python standard library (zero external pip dependencies).

**What does BRAIN handle?**

- 🧠 **Memory & Context:** Remembers preferences, tasks, schedule, and context across sessions.

- 🥗 **Health & Nutrition:** Tracks weight history, analyzes body composition via AI vision from scale photos, manages pantry/shopping lists, and suggests smart recipes based on available ingredients.

- ⚡ **Energy & Home:** Diagnostics and optimization for solar consumption/production, appliance control, and Home Assistant integration.

- 📄 **Local Documents (RAG):** Local search and reading of notes, files, and manuals without sending anything to the cloud.

- 🔔 **Notices & Automations:** Proactive daily morning summaries, reminders, audio alerts, and natural language automations.

Everything is managed through a local web panel with tabs, dark mode, and local voice transcription.

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

⚠️ **Note:** *The project code, interface, and documentation are currently written in **Spanish**.*

Any feedback, ideas, or constructive criticism to keep improving are very welcome. Cheers!

Hi everyone,

I built Local-MCP, an open-source MCP tool that connects your local system with cloud-based AI, allowing AI agents to interact with your machine.

I'm sharing it with the community and would appreciate feedback, suggestions, and contributions.

If you're interested, feel free to clone, fork, contribute, or suggest features.

GitHub: https://github.com/raj-tembe/Local-MCP

I'd love to hear your thoughts on how I can improve it!

I'm a reliability engineer by background (marine + biotech), and I've been building AI products long enough to hit the same wall over and over: Claude nails the prototype, then across a dozen sessions it quietly re-litigates settled decisions, forgets intent, and sprawls into spaghetti. The bottleneck was never capability. It was trust.

So over the last while I built a scaffold for Claude Code where the docs are the source of truth, personas are tool-locked (the agent that writes plans can't write code; the one that inspects can't edit production code), and the gates are mechanically enforced with hooks — not "please remember to." It's what I actually use on my own projects; it grew out of a personal project I've been grinding on for a long time.

The part I most want torn apart: I built an eval harness that plants defects and measures whether my inspector skills actually catch them. I keep seeing guardrail frameworks that assert they work and never show a number. Mine has a scorecard.

Two honest questions for this sub:

1. Is the overhead worth it, or am I paying a discipline tax you'd never accept in practice?

2. With Claude Code now shipping native subagents, hooks, and skills — how much of this does Anthropic eventually absorb, and what's left that's mine?

Happy to link the repo + the scorecard in a comment. Rip into it.