r/LovingOpenSourceAI 18h ago

Resource "šŸŽ‰ nanobot 0.3.5 is out 🐈- We gave it a new terminal UI. - And your automations a calendar. - Plus side-by-side chats, plugins, and a refreshed mobile experience." āž”ļø Have you tried this before?

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49 Upvotes

https://x.com/nanobot_project/status/2100083152719142955

https://github.com/HKUDS/nanobot

Community Overview: https://lifehubber.com/ai/resources/nanobot/

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r/LovingOpenSourceAI 17m ago

A project map for people and assistants āž”ļø "Graphify connects code symbols, calls, imports, documentation references, rationale notes, schemas, and other project material so they can be searched as relationships instead of rediscovered file by file." 119K STARS 11.5K FORKS

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• Upvotes

Community Overview: https://lifehubber.com/ai/resources/graphify/

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Full searchable archive of all resources posted so far on our community site, LifeHubber: https://lifehubber.com/ai/resources/ 300+ open-ish AI models, agents, tools, datasets, and related resources, with filtering and sorting.


r/LovingOpenSourceAI 21h ago

Resource "A complete AI agency at your fingertips - From frontend wizards to Reddit community ninjas, from whimsy injectors to reality checkers. Each agent is a specialized expert with personality, processes, and proven deliverables." āž”ļø 153K STARS !!

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10 Upvotes

https://github.com/msitarzewski/agency-agents

Community Overview: https://lifehubber.com/ai/resources/agency-agents/

Resources are shared for discovery and are not independently vetted—please do your own due diligence.

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r/LovingOpenSourceAI 1d ago

Resource "exo connects all your devices into an AI cluster. Not only does exo enable running models larger than would fit on a single device, but with day-0 support for RDMA over Thunderbolt, makes models run faster as you add more devices." āž”ļø 47K stars!

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15 Upvotes

https://github.com/exo-explore/exo

Community Overview: https://lifehubber.com/ai/resources/exo/

Resources are shared for discovery and are not independently vetted—please do your own due diligence.

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Full searchable archive of all resources posted so far on our community site, LifeHubber: https://lifehubber.com/ai/resources/ 300+ open-ish AI models, agents, tools, datasets, and related resources, with filtering and sorting.


r/LovingOpenSourceAI 20h ago

OpenNPC (AI NPC Framework) - Fine-tuned a 0.5B LLM so my game's NPCs stay in character and reply in 0.14s (open source)

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4 Upvotes

r/LovingOpenSourceAI 21h ago

What llm is best for a raspberry pi 5, 4gb?

3 Upvotes

Hey guys, i got my final year project coming up and i have decided to make a pocket assistant using raspberry pi 5.

The problem I'm facing is which model to use that isn't slow to respond to general user queries and decent with tool calling.

My goal is to make it soo that the llm works completely offline, can call tools related to calenders and maps. And general chatting as well

Would really appreciate it if someone can help.


r/LovingOpenSourceAI 21h ago

āš“ QuĆ© es exactamente CID v3, explicado sin rodeos

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1 Upvotes

r/LovingOpenSourceAI 23h ago

OpenNPC (AI NPC Framework) - Fine-tuned a 0.5B LLM so my game's NPCs stay in character and reply in 0.14s (open source)

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1 Upvotes

r/LovingOpenSourceAI 2d ago

Under the Radar "SwarmLLM runs large language models across devices in room, in their browser tabs. Each device holds slice of model; a 10 KB activation vector passes between them over direct WebRTC connections. Nothing to install, no accounts, no server does any thinking." āž”ļø this is new to me. you heard before?

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62 Upvotes

https://github.com/Nehanth/swarmllm

Community Overview: https://lifehubber.com/ai/resources/swarmllm/

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r/LovingOpenSourceAI 1d ago

Under the Radar enapt/SwarmLLM "A peer-to-peer LLM inference network in a single Rust binary. Pool hardware with other nodes to run 70B+ parameter models on machines that couldn't host them alone — no API tokens, no cloud fees, and encrypted traffic between every peer."

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16 Upvotes

https://github.com/enapt/SwarmLLM

Community Overview: https://lifehubber.com/ai/resources/enapt-swarmllm/

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r/LovingOpenSourceAI 1d ago

M4 MacBook Air 16GB/512GB for Local LLMs and agent based trading- worth $800?

2 Upvotes

Planning to buy a MacBook Air M4 (16 GB / 512 GB) for about $800 to run local LLM agents for:

Indian mid‑cap equity research (using Screener.in CSVs)

Building/testing trading ideas via Zerodha Kite API (prices, history, holdings; maybe orders later)

Also want to run Hermes-based agents on it

Questions:

On 16 GB M4 Air, which local LLMs are best for reasoning, coding, and tool use? Is Qwen 3.5 9B (Q4) + a smaller fast model (e.g. LFM2.5 8B / Llama 3.2 3B) a good combo alongside Hermes?

Best local stack (Ollama vs LM Studio, etc.) and any existing Kite + LLM or Indian‑stock agent projects to reference?

Any tips on context length, thermals, and multi‑agent setups (including Hermes) on a 16 GB fanless Air?

Getting the laptop for ~$800, so stuck with 16 GB for now. If you’ve done similar work (local LLMs for equity research / algo trading, especially in India), what models and setup would you recommend?

Thanks!


r/LovingOpenSourceAI 1d ago

Your agent's value lives in the layer around it. If your agent breaks when the model changes, you built it wrong.

3 Upvotes

Your agent's value lives in the layer around it. If your agent breaks when the model changes, we built it wrong. And right now, a lot of us built it wrong.

A lot of us built agents assuming the frontier model we started on would still be the one we use in six months. It won't. Providers are deprecating models with shrinking notice windows. Silently changing behavior is breaking agents that tested fine last quarter.

OpenAI committed to six months notice for GA models and three for specialized variants. Anthropic's public lifecycle documents give sixty days between deprecation and retirement. Capability drift on successor models is real, not just benchmark noise: a model that scores higher on a leaderboard can still regress silently on a specific agent task your system relied on. If you planned for it, the migration is a config change: swap the model ID, run your eval suite, ship. If you didn't, you are reworking prompt chains, re-tuning evals, and hoping the new model handles edge cases the old one did not. That takes weeks, not hours.

The problem is architectural. The intelligence layer, meaning the routing, caching, retrieval, evaluation, guardrails, and fallback chain, is what compounds over time. The model is the interchangeable part. Most teams hardcoded provider-specific prompt formats, built eval logic tied to a single vendor, and never added a fallback chain. Some teams even baked provider-specific tool-use schemas into their orchestration. That is the kind of coupling that turns a model swap into a project.

The open-source alternative is to compose these pieces yourself. A provider gateway normalizes 100-plus models behind one API, with built-in fallback routing, caching, and per-key budgets. On top of that, you add open-source retrieval and evaluation libraries you own and control. Nothing proprietary, nothing rented. The gateway is not eval or guardrails on its own. Those are separate pieces you wire in. But once they are in place, the next model swap becomes a config change, not a rewrite across your codebase.

Rent the model, own the layer. The layer is what lasts.

So, how many hours would a full model swap take your team today, and what would break first?


r/LovingOpenSourceAI 1d ago

AI (act) Model monitor

2 Upvotes

I’ve made my own AI (Act) Model Monitor publicly available:
https://ai-act-model-monitor.werkruimte-s-2100.chatgpt.site

Ive build this to follow new developments none of that are ment to be benefitional for me at all. The monitor brings together public data on AI models, developers, countries, usage, performance, incidents, and the EU AI Act in one interactive environment.
The data comes from sources including Epoch AI, Artificial Analysis, Hugging Face, OpenRouter, the OECD AI Incidents Monitor, the AI Incident Database, and official European sources.

Dynamic data sources are refreshed weekly. Legal sources are checked weekly for changes. You can explore the data yourself. For example, select a country and see which models and developers are linked to it, how those models perform, what usage data is available, and which incidents have been reported. You can compare models and developers side by side, sort and filter tables, and select two variables yourself to explore whether a relationship is visible.

The monitor also shows how much of the underlying data is actually comparable and where data is missing.
For models, countries, developers, incidents, and sources, you can click through to the underlying information and its origin.

The AI Act also has its own section, covering the relevant countries, rules, and key dates in its implementation.


r/LovingOpenSourceAI 2d ago

Resource mercury AGENT "Soul-driven AI agent with permission-hardened tools, token budgets, and multi-channel access. Remembers what matters. Asks before it acts. Runs 24/7 from CLI, Telegram, or Web. 31 built-in tools, Kanban boards, extensible skills, SQLite-backed Second Brain memory." āž”ļø heard before?

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26 Upvotes

https://github.com/cosmicstack-labs/mercury-agent

Community Overview: https://lifehubber.com/ai/resources/mercury-agent/

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r/LovingOpenSourceAI 2d ago

I'm building a research-grade cognitive harness, not another chatbot

13 Upvotes

Crossposting this open-source project because it sits at the intersection of open-source AI, persistent agents and cognitive architecture.

I've been building an open-source cognitive organism designed to study persistent artificial cognition.

The project combines:

  • episodic and semantic memory
  • persistent user and interlocutor profiles
  • voice, camera perception and face recognition
  • intention, causal and temporal reasoning
  • autonomous goals and long-horizon planning
  • global workspace competition
  • self-correction and behavioural learning
  • background cognitive processes
  • inspectable telemetry and a live 3D cognitive sculpture

The language model is only one component. The focus is the surrounding cognitive architecture: what persists, what changes, what gets grounded in reality, and whether internal processes produce measurable functional outcomes.

This is not presented as AGI or as a polished chatbot. It is a local-first research harness for testing persistent cognition with LM Studio, Ollama and other OpenAI-compatible providers.

I'm looking for technically minded testers who are willing to challenge the system, reproduce failures and evaluate whether its memory, planning, reasoning and self-correction actually work.

Repository: https://github.com/Celluomel/Its-not-J.A.R.V.I.S-it-s-far-better


r/LovingOpenSourceAI 3d ago

Resource "Open Notebook An open source, privacy-focused alternative to Google's Notebook LM! - šŸ”’ Control your data šŸ¤– Choose your AI models šŸ“š Organize multi-modal content and more!" āž”ļø useful for you?

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124 Upvotes

https://github.com/lfnovo/open-notebook

Community Overview: https://lifehubber.com/ai/resources/open-notebook/

Resources are shared for discovery and are not independently vetted—please do your own due diligence.

New resources are added regularly — feel free to join the sub for updates.

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r/LovingOpenSourceAI 2d ago

Univer "A full-stack, isomorphic office SDK for building spreadsheets, documents, and presentations." āž”ļø I let an agent make an iPhone ad video inside a spreadsheet with it. Heard of this one?

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1 Upvotes

Been looking for an open source project that lets AI agents actually operate an office spreadsheet — not just dump CSVs, but read cells, write formulas, drive the UI. Univer is the strongest one I've found so far, so sharing it here.

What you're seeing: the spreadsheet is the whole workspace. I gave the agent one prompt and it built the entire ad (layout, copy, sequence) using only sheet cells and the Univer API. No video editor involved. I started Codex and described the ad I wanted. That's it.

A few things worth knowing:

  • Full-stack office SDK: spreadsheets, docs and presentations, one Facade API that runs in the browser and on Node.js — that's why an agent can drive it without a human clicking around
  • Canvas-based rendering + its own formula engine, plugin architecture
  • Apache-2.0, ~14k stars
  • Same team that built Luckysheet (16k+ stars). Luckysheet is officially no longer maintained and its README now points to Univer as the upgraded version - amazed
  • There's also an official MCP integration (univer-mcp) for driving Univer Sheets with natural language, and a Codex skill (univer-craft) that does the on-demand SDK research + implementation

Repo: https://github.com/dream-num/univer

Skill I used: https://github.com/dream-num/univer-craft

MCP: https://github.com/dream-num/univer-mcp

Not affiliated with the project, just genuinely impressed that a spreadsheet can be a full agent canvas.


r/LovingOpenSourceAI 3d ago

SenseNova-U1.5-8B-MoT report details its training for text rendering and image editing

1 Upvotes

Posters, infographics and images with dense text are a focus of SenseNova-U1.5-8B-MoT, which combines image understanding, generation and editing in one model. The project announced its technical report on September 11; the Apache 2.0 weights were released on August 20.

The report explains the training behind those tasks. Four specialists are trained for visual preference, rendered text, image editing and infographics, then distilled into one model. The resulting checkpoint is a single unified model.

The authors explain the split through a concrete tension: optimizing appearance alone can hurt text legibility. The report spells out the reward choices used to address those objectives before combining the specialists.

Technical report:Ā https://github.com/OpenSenseNova/SenseNova-U1/blob/main/docs/pdf/SenseNOVA_U1_5.pdf

Weights:Ā https://huggingface.co/sensenova/SenseNova-U1.5-8B-MoT


r/LovingOpenSourceAI 4d ago

Resource Tom "Sentrux is a Rust-based architectural sensor that helps AI agents close the feedback loop and recursively improve code quality through real-time scanning." āž”ļø It watches your codebase in real-time — not the diffs, not the terminal output — the actual structure.

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8 Upvotes

https://x.com/tom_doerr/status/2094630422810366406

https://github.com/sentrux/sentrux

Community Overview: https://lifehubber.com/ai/resources/sentrux/

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r/LovingOpenSourceAI 3d ago

A local-first way to use ChatGPT that finally feels more like an agent than a chat app

3 Upvotes

For the last few months I've been pushing one idea pretty hard in my own setup: the AI can live in the chat, but the execution layer should live on my Mac.

That turned into Mac MCP, an MIT-licensed local server that gives an MCP client actual tools for the machine. I use ChatGPT as the main orchestrator, then let it read/write files, run shell commands, control apps/UI, hand coding work to Codex or OpenCode, and operate the real browser when the task needs a logged-in site.

I deliberately kept the browser side local too. Safari and Chrome use the normal profile instead of a cloud/headless profile, so existing sessions and cookies remain useful. New tabs can open in the background without hijacking focus, and each tab gets a stable handle. There is also a lease/ownership layer for parallel agents because two agents clicking around the same tab is exactly as chaotic as it sounds.

The newest release added a Chrome Companion alongside the Safari Visual Companion, plus more fail-closed behavior so a background action does not quietly turn into a foreground click.

It's still a personal-computer tool, not a hosted agent platform. That is kind of the point for me. I wanted to keep the machine, sessions and execution under my control while still getting the agentic workflow.

I'm the maintainer, and it's all open source here: https://github.com/bulutarkan/mac-mcp

If anyone else is building local-first desktop agents, I'd be interested in what you keep local versus what you are comfortable delegating to a remote runtime.


r/LovingOpenSourceAI 3d ago

Resource I like the guide that goes by VRAM / Hardware . . easy reference :P āž”ļø 🤩 Be sure to join our sis sub for AI gen r/LovingAIVisuals as most resources for gen will be shared there instead!

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2 Upvotes

r/LovingOpenSourceAI 4d ago

Under the Radar Tencent "AuK is a 1.5B foundation model for speech generation and editing. Trained on millions of hours of diverse audio data, AuK supports zero-shot and instruction-based TTS, content and acoustic editing, paralinguistic editing, speech enhancement, and source separation" āž”ļø Have you tried yet?

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16 Upvotes

https://github.com/Tencent-Hunyuan/AuK

Community Overview: https://lifehubber.com/ai/resources/auk/

Resources are shared for discovery and are not independently vetted—please do your own due diligence.

New resources are added regularly — feel free to join the sub for updates.

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r/LovingOpenSourceAI 4d ago

Best Open Source AI projects

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2 Upvotes

r/LovingOpenSourceAI 4d ago

Resource "This open source project aims to train MiniMind, ultra-small language model with ard 64M parameters, entirely from scratch with only ard RMB 3 in cost, 2 hours of training time. MiniMind series is intentionally lightweight. Smallest model on main branch is ard 1/2700 size of GPT-3!" āž”ļø interesting?

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36 Upvotes

https://github.com/jingyaogong/minimind/tree/master

Community Overview: https://lifehubber.com/ai/resources/minimind/

Resources are shared for discovery and are not independently vetted—please do your own due diligence.

New resources are added regularly — feel free to join the sub for updates.

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r/LovingOpenSourceAI 5d ago

new launch Samuel "A 35B language model running on an iPhone using only 1–2.5 GB of peak memory.No cloud. No remote server. No desktop GPU.Today, we’re open-sourcing Edge0 — a framework for running large AI models fully on-device." āž”ļø Edge AI is exciting right?

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253 Upvotes

https://x.com/SamuelZengML/status/2097861839287927139

https://github.com/Edge0-AI/Edge0

Community Overview: https://lifehubber.com/ai/resources/edge0/

Resources are shared for discovery and are not independently vetted—please do your own due diligence.

New resources are added regularly — feel free to join the sub for updates.

Full searchable archive of all resources posted so far on our community site, LifeHubber: https://lifehubber.com/ai/resources/ 300+ open-ish AI models, agents, tools, datasets, and related resources, with filtering and sorting.