LlamaParse

LlamaParse AI

LlamaParse AI

LlamaParse AI

LlamaParse AI

LlamaParse AI

LlamaParse AI

LlamaParse AI

LlamaParse AI

LlamaParse AI

LlamaParse AI

[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]

I integrated a local Llama 3.2 model to act as a dynamic Dungeon Master in my indie RPG.

Hey everyone, I am not trying to sell or self promote mainly just wanted to showcase a big project I've been working on ever since I started studying data science and artificial intelligence and integrating AI into workflows and using it as an augment to create things that were previously out of reach for so many people, because if used right it can become a second brain and not a crutch. I’m the solo dev behind Void Runner, an isometric ARPG/MOBA hybrid built in Python. I recently hit a wall with traditional procedural quest generation. Hand-crafting templates gets repetitive fast, and players quickly learn the patterns to these things whether you like it or not. To solve this, I built the "Void Caller AI", a system that uses a local, quantized Llama 3.2 model to act as a dynamic Dungeon Master. Instead of just generating random flavor text, the system uses a lightweight RAG (Retrieval-Augmented Generation) pipeline. It reads live server telemetry (who died, what items were looted, which bosses were defeated recently) and weaves those actual server events into the narrative of the quests it generates. Because it runs locally via Ollama on our backend, there are no crazy cloud API costs, and latency is kept completely manageable. Here is a simplified look at how the Python backend bridges the SQLite telemetry with the Llama 3.2 prompt: import json import ollama from sqlalchemy import text from database import SessionLocal def generate_dynamic_quest(difficulty: str, target: str): db = SessionLocal() # 1. Fetch recent server telemetry for context (RAG-lite) lore_context = "" try: # Grab recent server events to weave into the narrative recent_events = db.execute(text( "SELECT username, event_type, dungeon_type FROM ai_events ORDER BY id DESC LIMIT 3" )).fetchall() if recent_events: events_str = "; ".join([f"Runner '{r[0]}' triggered a '{r[1]}' in '{r[2]}'" for r in recent_events]) lore_context = f" Incorporate this recent live server telemetry into the lore: {events_str}" except Exception as e: pass # 2. Construct the prompt with strict JSON formatting constraints prompt = f"""You are the Void Caller, a sinister AI in a dark industrial sci-fi RPG. Create a dynamic PvE extraction quest of {difficulty} difficulty. Respond ONLY in valid JSON with keys: 'title' (string), 'description' (string, menacing), 'item_name' (string), 'quantity' (integer 1-15), 'boss_name' (string, optional). {lore_context}""" # 3. Stream to local Llama 3.2 response = ollama.chat( model='llama3.2', messages=[{'role': 'user', 'content': prompt}], format='json', options={'temperature': 0.8} ) return json.loads(response['message']['content']) By forcing the format='json' parameter, Llama 3.2 reliably outputs structured data that my game engine instantly parses into a playable quest objective. If a player just died to a specific boss, the AI will literally generate a bounty quest for the rest of the server to avenge them. Would love to hear if anyone else is using local LLMs for live game state generation! You can check out the results live in our Open Beta at [void-runner.online]. submitted by /u/xSoulR34per [link] [comments]

How do you know your document parser is ready for production? 🤔Existing benchmarks miss what AI agents actually need. That's the gap ParseBench, the first doc OCR benchmark for AI agents, fills. We

How do you know your document parser is ready for production? 🤔Existing benchmarks miss what AI agents actually need. That's the gap ParseBench, the first doc OCR benchmark for AI agents, fills. We'll unveil all the magic behind it in a live webinar 👇 https://t.co/odSaGMAlkz https://t.co/hjZGEol4df

New in LlamaParse: Latency Metrics is now live. For every Parse, Extract, and Classify job, you can now get a full latency breakdown. All broken down by tier. ⏱ Queue time ⚡Processing time 📊 Tota

New in LlamaParse: Latency Metrics is now live. For every Parse, Extract, and Classify job, you can now get a full latency breakdown. All broken down by tier. ⏱ Queue time ⚡Processing time 📊 Total latency There's also a new Metrics tab with a latency scatter plot and job https://t.co/wTB5tDv85P

RT @jerryjliu0: We built an AI agent for due diligence, with exact audit trails back to the source page, that you can use as a template wit…

RT @jerryjliu0: We built an AI agent for due diligence, with exact audit trails back to the source page, that you can use as a template wit…

Financial analysts spend ~70% of their time pulling numbers out of PDFs. We built a demo agent that ingests SEC filings and answers questions with exact citations highlighted on the original PDF page.

Financial analysts spend ~70% of their time pulling numbers out of PDFs. We built a demo agent that ingests SEC filings and answers questions with exact citations highlighted on the original PDF page. About 600 lines of Next.js. No vector DB. Just LiteParse.

We're live at @googleio! Thanks, @OfficialLoganK for the shoutout in the developer keynote. Lots of exciting features comining to the @GeminiApp API🔥 and we're exciting to provide the document inf

We're live at @googleio! Thanks, @OfficialLoganK for the shoutout in the developer keynote. Lots of exciting features comining to the @GeminiApp API🔥 and we're exciting to provide the document infrastructure for Google ecosystem builders. https://t.co/sv2xM2Jh0V

🚀 The team at @Google just released the Agents API, a service for building and running custom agents inside a sandboxed Linux environment, and we built a template that gives these agents access to Ll

🚀 The team at @Google just released the Agents API, a service for building and running custom agents inside a sandboxed Linux environment, and we built a template that gives these agents access to LlamaParse / LiteParse, enabling them to process unstructured documents https://t.co/cS6Ydyt9Kt

How do you know your document parser is ready for production? 🤔 Existing benchmarks miss what AI agents actually need. That's the gap ParseBench, the first doc OCR benchmark for AI agents, fills. We

How do you know your document parser is ready for production? 🤔 Existing benchmarks miss what AI agents actually need. That's the gap ParseBench, the first doc OCR benchmark for AI agents, fills. We'll unveil all the magic behind it in a live webinar👇 https://t.co/qPIslwCimz

That's a wrap at @aiDotEngineer Singapore 🇸🇬 Thanks for all the devs that tuned into our workshop, keynote, and executive dinner. See you in a few weeks at the world fair in SF 🌉 https://t.co/yY

That's a wrap at @aiDotEngineer Singapore 🇸🇬 Thanks for all the devs that tuned into our workshop, keynote, and executive dinner. See you in a few weeks at the world fair in SF 🌉 https://t.co/yY4C0iHcaS

RT @jerryjliu0: Many AI agents in finance rely on extremely high quality context engineering from documents 📑 They can be roughly divided…

RT @jerryjliu0: Many AI agents in finance rely on extremely high quality context engineering from documents 📑 They can be roughly divided…

RT @jerryjliu0: A new set of open-weight models is topping the leaderboard for document understanding 🔥 INF just released two models: Infi…

RT @jerryjliu0: A new set of open-weight models is topping the leaderboard for document understanding 🔥 INF just released two models: Infi…

Yesterday we committed a cardinal sin: two first-party events in NYC, back-to-back. We had to close registration for both early. Packed rooms. Strong vibes. No regrets. 💻 Laptops out — developer wo

Yesterday we committed a cardinal sin: two first-party events in NYC, back-to-back. We had to close registration for both early. Packed rooms. Strong vibes. No regrets. 💻 Laptops out — developer workshop led by Jerry Liu and Logan Markewich 🍷 Glasses up — AI engineer happy https://t.co/vm3mAOdHJg

Need document parsing that stays fully local and private? 👀 Meet liteparse-server, a self-hostable, open-source HTTP server for parsing documents and generating screenshots from PDFs, Office files,

Need document parsing that stays fully local and private? 👀 Meet liteparse-server, a self-hostable, open-source HTTP server for parsing documents and generating screenshots from PDFs, Office files, and images. ✅ 100% self-hosted ✅ Private by default ✅ Open source ✅ Built https://t.co/nYe2VYroBX

Ever wished your agent could read PDFs, images, and Office documents as easily as plain text? Or combine the safety of a secure sandbox with the full power of Bash access? We built exactly that. Me

Ever wished your agent could read PDFs, images, and Office documents as easily as plain text? Or combine the safety of a secure sandbox with the full power of Bash access? We built exactly that. Meet 𝘀𝗮𝗻𝗱𝗯𝗼𝘅𝗲𝗱-𝗹𝗶𝘁, a Rust 🦀 CLI agent that combines: - LiteParse, https://t.co/HsC6I22QDM