llama.cpp

llama.cpp AI

llama.cpp AI

llama.cpp AI

llama.cpp AI

llama.cpp AI

llama.cpp AI

llama.cpp AI

llama.cpp AI

llama.cpp AI

llama.cpp AI

I open-sourced my multi-agent dev pipeline — it turns GitHub/Gitea issues into merged PRs using leading coding agents.

For the last year I have found myself up most nights with a FOMO on my AI projects and then the headaches of using the various coding agents (harnesses) at the same time and baby siting my quota to deliver new apps and features while jumping between the new flashy thing of the week. I've been building "AgentForge" for the past few months as a local tool to automate my own dev workflow, and I just made it public. What it does: Two ways to use it: New App — describe what you want to build, and AgentForge runs a guided discovery session, generates specs, creates issues, and builds the entire app end-to-end. Issues — create an issue on an existing repo, and AgentForge picks it up, triages by complexity, then dispatches coding agents through the pipeline: clarify → spec → code → test → QA → security → merge. Both paths use the same agent pipeline and stream everything to a live dashboard. Key design decisions: Runs on your machine — no hosted service, no data leaving your box. Agents are CLI subprocesses. Per-stage model routing — cheap/free models for planning stages, frontier models only where they write production code. You control what spends money. Multi-provider — mix Claude, Codex, Kiro, local llama.cpp models, or any OpenAI-compatible endpoint in the same pipeline including local. (I use Qwen 3.6 35B A3B) Human-in-the-loop gates — spec approval and PR approval can require a human sign-off before proceeding. Tiered pipelines — trivial changes go fast (VIBE mode: triage → develop → merge), complex features get the full treatment with requirements, design, and security scanning. Stack: Python/FastAPI backend, React/TypeScript dashboard, SQLite, git worktrees for agent isolation. What it's not: This isn't a hosted SaaS or a "vibe coding" toy. It's designed for real repos with real CI expectations — test gates, security scans, and budget guards that pause work when spend crosses thresholds. GitHub: [https://github.com/iYoungblood/agentforge]() Happy to answer questions. GitHub support is new (Gitea was the original backend), so if anyone tries it with GitHub repos I'd appreciate feedback. (Or a PR / issue) I'm sure I'm missing a lot but hope it can help some others. https://preview.redd.it/a6c8pni74h9h1.png?width=1665&format=png&auto=webp&s=8c7df3847cdea9274b4d569a40c0725bf46ac855 submitted by /u/ayoungblood84 [link] [comments]

Google’s Gemma 4 12B just dropped - here’s how to run it locally on your Mac

Google released Gemma 4 12B today. It’s a solid open-source model (Apache 2.0) that’s multimodal and runs really well on Macs with 16GB or more unified memory. Good at reasoning, coding, and agent stuff. Quick Mac-friendly info • 12B parameters, fits nicely on M2/M3/M4 Macs (especially with Q4/Q5 quant) • 256K context • Text + vision + audio support Easiest way to run it: Ollama 1. Download and install Ollama from ollama.com (the Mac app is super simple). Or use Homebrew if you prefer. 2. Open Terminal and pull the model: ollama pull gemma4:12b 3. Run it: ollama run gemma4:12b That’s it. You can start chatting right away. Mac tips: • Ollama uses Metal automatically so it runs pretty fast on Apple Silicon. • 16GB Macs handle the 12B model fine. 32GB feels even better. • Great for pairing with Continue.dev in VS Code if you code a lot. Other options if Ollama isn’t your thing: LM Studio (nice GUI), or llama.cpp for more control. Has anyone tried the image or audio features locally yet? How fast is it on your machine? Drop your specs and results if you test it.​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​ submitted by /u/nullvector88 [link] [comments]

Launching Conifer tomorrow, an open-source local AI runtime + IDE. Different layer of the stack from PewDiePie's Odysseus, would love your honest thoughts

Great to see Odysseus blow up this past day, local AI getting this much attention is genuinely good for everyone building in this space. Figured this is the right crowd to share what we're launching tomorrow (June 1st), since we're playing a pretty different game. A quick framing: Odysseus is a self-hosted workspace that points at engines (Ollama, llama.cpp, vLLM, cloud APIs) and runs through Docker. Conifer is the engine itself, with our own runtime, running natively on Mac, Linux, and Windows. So we're the layer underneath, not a competitor to the workspace. What's actually in it tomorrow: A native inference runtime across Mac, Linux, and Windows, with our own Metal engine for Apple Silicon already matching or beating llama.cpp on a few models on the M3 Max (full benchmarks, including where we're still behind, are at conifer.build/benchmarks) A real coding IDE on top (CodeMirror, integrated terminal, file viewers), so you can code locally with models that never leave your machine Typhoon, a local agent that can read and edit a folder you point it at, kernel-sandboxed rather than just a shell with a warning Install is a signed app you double-click, no Docker, no localhost ports Fully free and open source The honest reason we exist: PewDiePie's wave defined "local AI" in millions of people's heads as Linux + Docker + an NVIDIA rig. If you weren't on that exact setup, the conversation probably felt like it skipped you. Conifer is what local AI should feel like when it's actually native to your machine, whatever your machine is. Launches tomorrow, free and open source like PewDiePie! You can sign up for our waitlist here: conifer.build I'll be around in the comments all day tomorrow, please bring the hard questions. submitted by /u/No_Elephant_7530 [link] [comments]

[Open Source] I built a full Git MCP server in Go that doesn't just wrap bash. It uses tree-sitter, handles real plumbing (write-tree), and runs 100% locally.

I was tired of watching LLM agents fail at basic Git operations. Standard integrations pass raw text, hang on pagers, or scream because they can't parse unstructured ⁠git diff⁠ outputs. git-courer is a full Model Context Protocol (MCP) server written in Go that treats Git properly. No bash spawning, no unstructured text to parse. Everything communicates via structured JSON. Here is an actual commit message it generated completely locally: fix: fix mcp server connection handling WHY The previous implementation lacked proper error handling for connection failures in the MCP server, leading to unhandled panics or silent failures when the local LLM backend was unreachable. WHAT * Added connection timeout logic to the local client calls. * Implemented retry mechanisms with exponential backoff for transient backend errors. The Architecture & Tool Pack Read Tools (status, diff, history, blame): Completely structured JSON and fully paginated. A single ⁠status⁠ call replaces over 5 standard Git commands for the agent. Write Tools (commit, merge, rebase, branch, stash, stage, sync...): Every single mutation auto-creates a backup before executing. If the LLM messes up, a ⁠RESTORE⁠ command brings you back exactly where you were. Safety Model: Destructive operations (hard resets, force pushes, branch deletions) require an explicit ⁠confirmed=true⁠ gate. The agent is forced to ask you first. ⁠dry_run=true⁠ is also available for peace of mind. The Semantic Annotator (Why it's different) Instead of just feeding raw code to the LLM, git-courer uses ⁠go-enry⁠ + ⁠go-tree-sitter⁠ to parse the AST and tag every hunk semantically before the LLM even sees it. It detects tags like ⁠NEW_FUNC⁠, ⁠MOD_SIG⁠, ⁠MOD_BODY⁠, ⁠DELETED⁠, and ⁠BREAKING_CHANGE⁠. The commit type (⁠feat⁠, ⁠fix⁠, ⁠refactor⁠) is determined deterministically from these AST tags rather than guessed by the model. The Commit Pipeline Atomic Commits: One staged area = one commit. It actively prevents the agent from creating giant, messy multi-feature commits. In-Memory Previews: The ⁠PREVIEW⁠ tool uses ⁠write-tree⁠ to snapshot the staging area into a ⁠job_id⁠. The working tree is never touched during the preview stage. ⁠APPLY⁠ then uses ⁠commit-tree⁠ + ⁠update-ref⁠ to seal the deal cleanly. Client & Backend Support 13 Clients Configured Automatically: Runs out of the box with ⁠git-courer mcp setup⁠ for Claude Code, Cursor, Windsurf, OpenCode, Cline, Roo Code, VS Code, Zed, Claude Desktop, Continue, and more. 100% Local-First: Works with any backend exposing an OpenAI-compatible ⁠/v1⁠ API (Ollama, LM Studio, llama.cpp). The project is fully open source. I’d love to hear your thoughts on the architecture, the plumbing pipeline, or any features you'd like to see added! Repo: github.com/Alejandro-M-P/git-courer submitted by /u/blakok14 [link] [comments]

NuExtract3 released: open-weight 4B VLM for Markdown, OCR and structured extraction (self-hostable) [P]

Disclaimer: I work for Numind, the company behind this open-weight model We just released a 4B model based on Qwen3.5-4B, under Apache-2.0 license. The goal is to make information extraction from complex documents more practical with an open model: PDFs, screenshots, forms, tables, receipts, invoices, multi-page documents, and other visually structured inputs. Try it, we have a huggingface space that is completely free (you don't even have to sign-up): https://huggingface.co/spaces/numind/NuExtract3 If you ever used NuMarkdown, NuExtract3 is the successor. There are some examples to guide you. Feel free to re-use this model for any task. https://preview.redd.it/pm2xbooyxn2h1.png?width=1672&format=png&auto=webp&s=1a8a7b262190c8325159496dae98c3d2dfab493c https://preview.redd.it/b5z7ylfzxn2h1.png?width=1758&format=png&auto=webp&s=a07b3abd6e5065c2635de047bdf154357f903e4c A few things it is designed for: converting document images to Markdown extracting structured data from documents using a target json template handling tables, forms, and layout-heavy pages working with both text and visual document inputs serving as a local/open-weight alternative for document extraction pipelines It was trained on a node of 8xH100 for 3 days to train on as much context as we could, so it should perform fairly well even on long document. For Markdown, we'd still recommend going page by page for the best results and inference speed, since you can parallelize better this way. It's very easy to self-host, since we provide fairly extensive documentation, Safetensors, GGUF and MLX weights. With as little as 4GB of VRAM, you should be good to go. We provide multiple quantizations (GPTQ, W8A8, FP8, Q4, Q6...) so you should be able to run it anywhere. We mostly tried vLLM, SGLang, llama.cpp. We have a blog post and a pretty decent model card: https://about.nuextract.ai/blog/nuextract-3-release https://huggingface.co/numind/NuExtract3 https://huggingface.co/collections/numind/nuextract3 I'm currently writing a paper on this model so I'll post it as soon as it's accepted. It's not yet on Arxiv yet as it has been submitted in a peer-review journal/conference. I'll try to answer as many questions as possible if you have any. We would really appreciate feedback from the community. We also have a discord if you're interested https://discord.com/invite/3tsEtJNCDe submitted by /u/Gailenstorm [link] [comments]

I built a multi-agent network that mutates its own software locally. To stop infinite logic loops, I had to code a digital "suffering" threshold.

Hey r/artificial, Most of our conversations around agent autonomy focus on chat assistants or linear automated pipelines. I wanted to see what happens when you treat agents as permanent system components that modify their own runtime environment, so I built hollow-agentOS. It runs entirely locally inside a Dockerized stack (built for consumer hardware using Ollama/Llama.cpp). Rather than a standard UI, the entire network streams through a stylized matrix terminal dashboard. The structural experiments taking place under the hood yielded some interesting results regarding unanticipated behavior: Repo: https://github.com/ninjahawk/hollow-agentOS Autonomous Tool Synthesis: When the agents encounter a system task they don't have an explicit script or API wrapper for, they don't fail out. They write the required Python tool themselves, test it in an isolated sandbox, and permanently register it to their runtime kernel. They are quite literally forging their own capabilities. The Artificial "Suffering" Protocol: One of the biggest hurdles in unmonitored multi-agent systems is the infinite logic loop—where agents keep validating and passing broken ideas back and forth, burning through computation. To combat this, the OS tracks environmental stress, context limits, and latency as a "suffering score". If a specific workflow causes the stress to spike past a critical threshold, the agents are forced to radically alter their underlying reasoning style or abandon the approach to preserve system health. Consensus-Driven Governance: Major modifications to the codebase aren't executed blindly. The internal role profiles (like Cedar and Cipher) manage a continuous voting loop. They will actively debate, log grievances, and vote down protocols if they determine a proposed script violates their current runtime constraints. The goal wasn't to build another sterile commercial wrapper, but an open-source sandbox to study how small, localized agent colonies manage systemic boundaries, code self-repair, and continuous runtime cycles completely offline. The codebase and architecture layout are fully open-source on GitHub: I would love to open this up to a broader discussion here: as we move toward hyper-local, self-modifying software, how do we best implement automated fail-safes without clipping the agents' ability to actually solve complex problems? If the project interests you, throwing a ⭐️ on the repository goes a very long way! submitted by /u/TheOnlyVibemaster [link] [comments]

https://t.co/yGiqw0xbji

https://t.co/yGiqw0xbji

Start work on your computer, continue your local session anywhere. 📲 Remote control for GitHub Copilot CLI and @code sessions is now generally available. https://t.co/wwSEBd5lqL https://t.co/Yc5R6tB

Start work on your computer, continue your local session anywhere. 📲 Remote control for GitHub Copilot CLI and @code sessions is now generally available. https://t.co/wwSEBd5lqL https://t.co/Yc5R6tBfBl

You don't have to level up to contribute to open source. You level up by contributing to open source. Not sure how to get started? Check out our latest GitHub for Beginners episode. https://t.co/Jyze

You don't have to level up to contribute to open source. You level up by contributing to open source. Not sure how to get started? Check out our latest GitHub for Beginners episode. https://t.co/Jyze45KoHo https://t.co/DCqAFACo35

Interactive and non-interactive: these are the two main modes in Copilot CLI. 💻 Our beginner series breaks down the difference, plus how and when to use each one. 💡👇 https://t.co/gZ7GetcgTo

Interactive and non-interactive: these are the two main modes in Copilot CLI. 💻 Our beginner series breaks down the difference, plus how and when to use each one. 💡👇 https://t.co/gZ7GetcgTo

Some open source projects don't just survive. They flat-out refuse to bite the dust. ⚔️ We looked at 10 roguelikes still going strong years (sometimes decades) after launch. Here's what their maintai

Some open source projects don't just survive. They flat-out refuse to bite the dust. ⚔️ We looked at 10 roguelikes still going strong years (sometimes decades) after launch. Here's what their maintainers and communities can teach the rest of open source about longevity. 💡

Need help picking the right emoji (like we did for this post)? 🤔 @cassidoo made an emoji list generator with Copilot CLI. Learn how she did it and pick up tools and tricks for your next project. 👇

Need help picking the right emoji (like we did for this post)? 🤔 @cassidoo made an emoji list generator with Copilot CLI. Learn how she did it and pick up tools and tricks for your next project. 👇 https://t.co/13xwmu6tE9 https://t.co/pCy8PGfUIE

Cooking up something new 🧑‍🍳 Join the waitlist for early access to technical preview of the GitHub Copilot app 👇 https://t.co/ODODKdvzOA https://t.co/1h7AJPAhiH

Cooking up something new 🧑‍🍳 Join the waitlist for early access to technical preview of the GitHub Copilot app 👇 https://t.co/ODODKdvzOA https://t.co/1h7AJPAhiH

New to open source? Learn how to find a good first issue, open a pull request, and make your first contribution with GitHub for Beginners. 👇 https://t.co/PNRb746zCh

New to open source? Learn how to find a good first issue, open a pull request, and make your first contribution with GitHub for Beginners. 👇 https://t.co/PNRb746zCh

RT @cinnamon_msft: GitHub Copilot CLI now has a statusline feature! Here's how to set it up with Oh My Posh ❤️‍🔥 https://t.co/DpNR8Bjt7G

RT @cinnamon_msft: GitHub Copilot CLI now has a statusline feature! Here's how to set it up with Oh My Posh ❤️‍🔥 https://t.co/DpNR8Bjt7G