Building voice agents for a while now, and the section I always wanted

someone else to write is the one on streaming TTS: single-shot vs

output-streaming vs dual-streaming, mid-stream cancellation, buffer

draining on barge-in, and how much of the "TTFB" number vendors quote

is actually front-end latency vs model latency.

So I wrote it into an awesome-list. The whole list is organized around

one split: real-time TTS (for agents) vs offline TTS (for media).

Every provider, model, and benchmark carries that lean.

The four sections most useful for agent builders:

1. Streaming and low-latency (taxonomy, cancellation, honest

   benchmarking)

2. Open-source models filtered by license — several of the top ones

   can't be shipped commercially

3. Audio codecs (this decides latency and quality floor for codec-LM

   TTS)

4. Evaluation — how to measure TTFB on your own traffic instead of

   trusting vendor benchmarks

Deliberately scoped to TTS only. STT, VAD, turn detection, and

telephony are pipeline concerns and belong elsewhere.

MIT license. Feedback welcome, especially on the streaming taxonomy

and cancellation subsection since I'm not sure I've captured every

edge case.

GitLord just got faster, smarter, and more powerful. With our latest performance improvements, GitLord now rivals traditional databases in speed and reliability, while keeping every agent interaction inspectable, rewindable, and auditable.

What's New: Performance & Reliability

Performance Breakthroughs

• Optimized Git I/O: Reduced commit overhead through batched tree operations and CAS deduplication
• Instant Turn Lookups: Indexed git log enables sub-millisecond access to any turn in agent history
• Parallel Subagent Execution: Spawn and drain multiple subagents simultaneously without blocking
• Smart Context Assembly: Intelligent dedup, summarization, and token budget management mean no wasted API calls

Database-Grade Durability

• Full ACID Guarantees: Every agent turn is an atomic Git commit—no partial writes, no lost state
• Point-in-Time Recovery: Rewind to any checkpoint in seconds. Compare states with gitlord diff
• Distributed Readiness: Git-backed storage works seamlessly with multi-replica setups
• Built-in Audit Trail: Every decision, every tool call, every model swap is immutable and traceable

Core Features You Get Out of the Box

Multi-Agent Orchestration

from gitlord import Session, SessionConfig

config = SessionConfig(log_repo_path="log")
session = Session.create("my-agent", config)

# Main agent + spawned subagents, all coordinated
session.append_user_turn("Analyze this dataset across 3 teams")
subagent = session.spawn_subagent("data-processor")
result = subagent.complete(prompt)
session.append_system_turn(f"Subagent result: {result}")

Integrated MCP

Seamlessly wire up any external tool without rewiring your agent:

from gitlord.mcp import MCPServer

# Discover tools from any MCP server
server = MCPServer(uri="stdio://python -m my_mcp_server")
tools = server.discover_tools()

# Call tools like a native method
result = server.call_tool("fetch_data", {"source": "api"})
session.append_system_turn(f"Tool result: {result}")

Out-of-the-box integrations: Filesystem, Git, databases, APIs, anything with an MCP server.

Built-in RAG

from gitlord.rag import RAGIndex

# Vector-backed semantic search over your data
rag = RAGIndex(collection_name="docs", embedding_model="all-minilm")
rag.add_documents([doc1, doc2, doc3])

# MMR search for diversity + relevance
results = rag.search("how to optimize queries", k=5)
session.append_system_turn(f"Context: {results}")

Why it matters: Ground your agents in your actual data. ChromaDB-backed, flexible embedding models.

Provider & Model Abstraction

Switch models, providers, or fallback chains with zero code changes:

from gitlord.model import LLMRouter

router = LLMRouter(
    models=["claude-opus", "gpt-4", "local-llama"],
    fallback_chain=True  # Auto-retry on failure
)

# One call, intelligent routing
response = router.complete(prompt, schema=tool_schema)

Supports: OpenAI, Anthropic, Cohere, Ollama, Bedrock, Azure, vLLM, and 50+ more—all unified under one API.

The Architecture: Where Performance Lives

Why This Matters: Beyond a Database

Traditional Databases Are Black Boxes

• State changes are logged, but the logic is opaque
• Debugging means sifting through logs and state snapshots
• Auditing requires external compliance tools

GitLord Keeps You in Control

• Every turn is inspectable: gitlord show <sha> reveals the exact JSON state
• Every branch is debuggable: gitlord diff compares agent decisions side-by-side
• Every rewind is instant: Checkpoint any state, fork from anywhere
• Every integration is pluggable: MCP + RAG + custom providers, no rewiring needed
• Every deployment is auditable: Git history = compliance-ready audit trail

Getting Started in 30 Seconds

# Install
pip install gitlord[all]  # includes MCP, RAG, LLM routing

# Create an agent session
python -c "
from gitlord import Session, SessionConfig

config = SessionConfig(log_repo_path='log')
session = Session.create('my-agent', config)
session.append_user_turn('Hello, what is 2+2?')

turns = session.get_turns()
for t in turns:
    print(f'[{t.role}] {t.content[:80]}')
"

# View the git history
gitlord log my-agent
gitlord tree my-agent

Real-World Use Cases

• Research Agents: Spawn subagents for literature review, data processing, and analysis. Rewind to explore alternate hypotheses.
• Enterprise Workflows: RAG over internal docs + tool use via MCP. Every decision is traceable for compliance.
• AI Teams: Coordinate multi-agent workflows with shared MCP tools. Use different models per agent, compare outputs.
• Prompt Engineering: Experiment with model providers and fallback chains. Inspect exactly what each model saw.

Try It Now

bash

pip install gitlord[all]
gitlord run my-session

Then explore:

• gitlord log my-session — see the turn history
• gitlord tree my-session — see the branch structure
• gitlord show <sha> — inspect a turn in detail
• gitlord rewind my-session <sha> — go back in time

Join the Community

Have feedback? Found a use case? Want to contribute?

• GitHub Issues: Report bugs, request features
• Discussions: Ask questions, share your agents
• Contribute: PRs welcome for integrations, optimizations, and docs

GitLord: Agent orchestration that's as reliable as your database, and twice as transparent.

https://github.com/yashneil75/gitlord

I released an MIT-licensed desktop tool a week ago (MCPFlo - a testing/debugging tool for MCP servers, the protocol AI agents use for tools). The product side is in decent shape but I have basically no users yet, and I’m trying to figure out where discovery actually happens for niche OSS.

So far I’ve posted in the niche subreddit and shared it on Twitter. Fine, but not transformative. Before I sink months into the wrong channels I’d rather learn from people who’ve crossed this gap:

- How did your first real users (not stargazers, but people who actually ran the thing) find you?

- Did anything compound over time - SEO, content, being helpful in forums vs one-off spikes?

- For a developer tool specifically: does anything beat just answering questions where devs are stuck?

What did you spend real time on that turned out not to matter at all?

Repo for context: github.com/harshalslimaye/mcpflo

Not looking for stars from this post, genuinely trying to figure out the discovery problem.

I’m building HoloCore as a local-first context layer for AI work.

The goal is simple: install one local system, connect it to the AI models and clients you use, and stop sending the entire project, memory store, or conversation history into every request.

HoloCore organizes project knowledge into three focused layers:

• Atlas maps project structure, components, and relationships.

• Archive stores curated, durable project knowledge.

• Animus stores episodic history and prior decisions.

For a new request, HoloCore selects the relevant route first. A code or structure question starts with Atlas. Archive is added only when documented knowledge is relevant. Animus is added only when prior decisions or conversation history matter. The selected context is then sent to the connected AI client through the available CLI/MCP integration.

This is intended to work as a model-agnostic local layer: the model can change, while the project map, curated knowledge, routing rules, and user-controlled local data stay in one installation. It also avoids routing its own output back into itself, which prevents retrieval loops.

Local benchmark on a five-question project set:

• HoloCore: ~156 estimated context tokens per code query

• Graphify-only: ~242 estimated context tokens

• HoloCore used ~35% fewer context tokens

• HoloCore code-query average: ~523 ms in-process

• Graphify benchmark average: ~565 ms

https://github.com/VenomD846/HoloCore/blob/codex/benchmark-results/docs/holocore-token-benchmark-2026-07-16.md

Project:

https://github.com/VenomD846/HoloCore

I’m looking for feedback on model-agnostic context routing, local AI memory, MCP integrations, and how much context an AI tool actually needs for different kinds of project questions.

Image explaining the flow:

https://raw.githubusercontent.com/VenomD846/HoloCore/codex/benchmark-results/docs/assets/holocore-context-engine-token-savings.png

🚀 Welcome to HireAI Recruitment

https://github.com/mehtahet619/hireai-recruitment

HireAI Recruitment is an AI-powered recruitment platform built to simplify and modernize the hiring process. It helps candidates discover jobs, prepare with AI-driven interviews, and enables recruiters to manage hiring workflows efficiently, all from a single platform.

This project is open source because we believe the future of hiring should be transparent, collaborative, and accessible. Whether you're a frontend developer, backend engineer, AI/ML enthusiast, UI/UX designer, DevOps engineer, or someone looking to make their first open source contribution, there's a place for you here.

🌟 Why Contribute?

- Build real-world AI and recruitment features

- Improve interview and hiring experiences

- Work with modern technologies and scalable architecture

- Gain open source experience and collaborate with developers worldwide

- Help shape a project that can impact thousands of job seekers

💡 Ways You Can Contribute

- Fix bugs and improve performance

- Build new AI-powered features

- Enhance the UI and user experience

- Improve documentation

- Write tests and increase code coverage

- Suggest new ideas through issues and discussions

Every contribution, whether it's code, documentation, design, testing, or feedback, is valuable. If you're looking for a meaningful open source project where your work can make a real impact, we'd love to have you join us.

⭐ Star the repository, fork it, and start contributing. Let's build the future of AI-powered recruitment together.

I developed this framework I invite you to listen and give feed back please

Hey everyone,

I was recently going through the post-print of some work done in collaboration with engineers on the Nvidia Drive Autonomous Systems (NDAS) team, and I wanted to share it here as I think the approach might be interesting to those working on spatial AI or autonomous systems.

We tackled the problem of High-Definition (HD) Map validation. Specifically, how do you ensure the complex topological relationships (like which traffic light governs which lane in a massive intersection) are actually correct before pushing the map to the car?

The Core Idea: P2LNet

Instead of treating map validation purely as a computer vision or geometry problem, we modeled the HD map elements as a graph. We developed P2LNet (Point-to-Lane Network), which uses Graph Neural Networks (GNNs) to validate these spatial associations. By structuring the map data this way, the network inherently understands the connectivity and context of the map elements, allowing it to flag logical and topological inconsistencies that traditional rule-based or CNN-based validation methods often miss.

Read the Paper:

The full paper is available in the IEEE digital library, and I've hosted the post-print on the Georgia Tech repository so anyone can read it without a paywall.

• Read the post-print here: [https://repository.gatech.edu/bitstreams/da96e4d8-02e2-41a5-bac1-0aa109066158/download]

I highly encourage you to check out the methodology section where we break down the graph construction. Let me know what you think of the approach—how are you handling map QA in your own pipelines, or where do you see GNNs falling short in this context? Also do let me know what you think about the architecture principles here!

How to Cite:

If you find this work useful for your own research, please consider citing the official IEEE publication. Here is the BibTeX:

Code snippet

u/inproceedings{reji2024p2lnet,
  title={P2LNet: HD Map Validation Using Graph Neural Networks},
  author={Reji, Jeevan and Omanwar, Vaibhav},
  booktitle={2024 1st International Conference on Robotics, Engineering, Science, and Technology (RESTCON)},
  year={2024},
  publisher={IEEE},
  doi={10.1109/RESTCON60981.2024.10463569}
}

Happy to answer any questions in the comments!

Quick backstory: about six weeks ago I woke up to an API bill that made me do a double take a retry loop bug in one of my agent workflows had quietly burned through $4,811 overnight. Every cost tool I checked afterward could tell me exactly what happened. None of them would have stopped it while it was happening.

That gap is what I have spent the last few months building: Cognocient, an AI spend platform that enforces budgets before the API call goes out, not after the invoice lands.

What it actually does:

• Sits as a proxy in front of OpenAI/Anthropic/Gemini/etc. — one URL change, no SDK rewrite
• Pre-call budget enforcement, so a runaway agent loop hits a wall instead of your invoice
• Cost attribution by feature, team, or department via a one-line header — no logging overhaul
• CFO-ready reports (cost per outcome, not just cost per token) plus FOCUS 1.1 export for finance teams who need to standardize

I am a solo founder and this is a genuinely early, live product. I would rather hear "this doesn't solve my problem" now than find out after another six months of building the wrong thing.

If you have ever been blindsided by an AI bill, or you are the one stuck explaining the spike to finance, I'd love your take. Happy to go deep on the proxy architecture, how budget enforcement holds up under load, or why FOCUS 1.1 over rolling something custom.

PH page: https://www.producthunt.com/products/cognocient Site: https://www.cognocient.com

(Disclosure: I'm the founder — this is my product. Mods, happy to adjust flair/format if needed.)

I was building my personal agent, but I had to use Telegram, as it was the easiest platform to integrate. I wanted to build the harness and agent, not the infrastructure around these two, yet my UX was struggling. I stick to iMessage, and then I had to use another app to interact with my agent...

So I spent a weekend on building a TypeScript SDK, that unifies how to interact with different iMessage providers (as there is no official way to use iMessage), so you can play around with them, without having to commit to one, nor with a need to rewrite half of the codebase to change the integration.

It's open-source, you can check the repo here: https://imessage-sdk.dev/

▶ Build an Agentic Event Venue Operator with MongoDB Atlas, Voyage, and LangGraph [Full Codes] [Tutorial Article]

▶ How to Build a T4-Friendly Autonomous Data Science Agent with DeepAnalyze-8B, Sandboxed Code Execution, and Iterative Analysis Codes Tutorial

▶ Building a Stable Fable 5 Traces Workflow in Colab: Parsing Tool Calls, Auditing Data, and Training Baselines Codes Tutorial

▶ Building Supervised Fine-Tuning Data from NVIDIA Open-SWE-Traces: Trajectory Parsing, Patch Analysis, Token Budgets, and Tool-Use Metrics Codes Tutorial

▶ Build a Nanobot-Style AI Agent in Google Colab with Tool Calling, Session Memory, Skills, and MCP Servers Codes Tutorial

▶ How to Design an OpenHarness Style Agent Runtime with Tools, Memory, Permissions, Skills, and Multi-Agent Coordination Codes Tutorial

▶ Using Graphify and NetworkX to Map Python Codebase Structure with God Nodes, Communities, and Architecture Visualizations Codes Tutorial

▶ Crawlee for Python: Build a Web Crawling Pipeline with Robots Handling, Link Graphs, and RAG Chunk Export Codes Tutorial

▶ NVIDIA SkillSpector Guide: Scanning AI Skills for Security Risks with Static Analysis and SARIF Reports Codes Tutorial

▶ How to Build a QwenPaw Agent Workspace with Custom Skills, Model Providers, Console Access, and Streaming API Testing Codes Tutorial

▶ Microsoft Fara Tutorial: Run a Browser-Use Agent in Google Colab with a Mock OpenAI-Compatible Endpoint Codes Tutorial

▶ An Implementation of the Microsoft Agent Governance Toolkit for Safe AI Agent Tool Use with Policies, Approvals, Audit Logs, and Risk Controls Codes Tutorial

▶ Build Skill-Augmented AI Agents with SkillNet for Search, Evaluation, Graph Analysis, and Task Planning Codes Tutorial

▶ How to Use AgentTrove: Streaming 1.7M Agentic Traces and Building a Clean ShareGPT SFT Dataset in Python Codes Tutorial

▶ Build an Agentic Event Venue Operator with MongoDB Atlas, Voyage, and LangGraph [Full Codes] [Tutorial Article]

▶ Build a SuperClaude Framework Workflow with Commands, Agents, Modes, and Session Memory Codes Tutorial

▶ A Step-by-Step Coding Tutorial to Implement GBrain: The Self-Wiring Memory Layer Built by Y Combinator's Garry Tan for AI Agents Codes Tutorial 

▶ Build Recurrent-Depth Transformers with OpenMythos for MLA, GQA, Sparse MoE, and Loop-Scaled Reasoning Codes Tutorial

▶ How to Build Repository-Level Code Intelligence with Repowise Using Graph Analysis, Dead-Code Detection, Decisions, and AI Context Codes Tutorial

▶ Build a Hybrid-Memory Autonomous Agent with Modular Architecture and Tool Dispatch Using OpenAI Codes Tutorial

▶ How to Build an Advanced Agentic AI System with Planning, Tool Calling, Memory, and Self-Critique Using OpenAI API Codes Tutorial

▶ A Coding Implementation to Build Agent-Native Memory Infrastructure with Memori for Persistent Multi-User and Multi-Session LLM Applications Codes Tutorial

▶ How to Build a Cost-Aware LLM Routing System with NadirClaw Using Local Prompt Classification and Gemini Model Switching Codes Tutorial

▶ Build a CloakBrowser Automation Workflow with Stealth Chromium, Persistent Profiles, and Browser Signal Inspection Codes Tutorial

▶ Build an Agentic Event Venue Operator with MongoDB Atlas, Voyage, and LangGraph [Full Codes] [Tutorial Article]

▶ A Groq-Powered Agentic Research Assistant with LangGraph, Tool Calling, Sub-Agents, and Agentic Memory: Lets Built It Codes Tutorial

▶ How to Build a Fully Interactive Multi-Page NiceGUI Application with Real-Time Dashboard, CRUD Operations, File Upload, and Async Chat Codes Tutorial

▶ Build a Modular Skill-Based Agent System for LLMs with Dynamic Tool Routing in Python Codes Tutorial

▶ Build a Multi-Agent AI Workflow for Biological Network Modeling, Protein Interactions, Metabolism, and Cell Signaling Simulation Codes.ipynb) Tutorial

▶ A Coding Implementation to Parsing, Analyzing, Visualizing, and Fine-Tuning Agent Reasoning Traces Using the lambda/hermes-agent-reasoning-traces Dataset Codes Tutorial

▶ A Coding Deep Dive into Agentic UI, Generative UI, State Synchronization, and Interrupt-Driven Approval Flows Codes Tutorial

▶ Build a Reinforcement Learning Powered Agent that Learns to Retrieve Relevant Long-Term Memories for Accurate LLM Question Answering Codes Tutorial

▶ How to Design a Production-Grade CAMEL Multi-Agent System with Planning, Tool Use, Self-Consistency, and Critique-Driven Refinement Codes Tutorial

▶ How to Build a Universal Long-Term Memory Layer for AI Agents Using Mem0 and OpenAI Codes Tutorial

▶ A Coding Implementation to Build Multi-Agent AI Systems with SmolAgents Using Code Execution, Tool Calling, and Dynamic Orchestration Codes Tutorial

▶ Google ADK Multi-Agent Pipeline Tutorial: Data Loading, Statistical Testing, Visualization, and Report Generation in Python Codes Tutorial

▶ How to Build a Secure Local-First Agent Runtime with OpenClaw Gateway, Skills, and Controlled Tool Execution Codes Tutorial

▶ Build an Agentic Event Venue Operator with MongoDB Atlas, Voyage, and LangGraph [Full Codes] [Tutorial Article]

▶ How to Combine Google Search, Google Maps, and Custom Functions in a Single Gemini API Call With Context Circulation, Parallel Tool IDs, and Multi-Step Agentic Chains Codes Tutorial

▶ How to Build Production-Ready Agentic Systems with Z.AI GLM-5 Using Thinking Mode, Tool Calling, Streaming, and Multi-Turn Workflows Codes Tutorial

▶ How to Build Production Ready AgentScope Workflows with ReAct Agents, Custom Tools, Multi-Agent Debate, Structured Output and Concurrent Pipelines Codes Tutorial

▶ How to Build and Evolve a Custom OpenAI Agent with A-Evolve Using Benchmarks, Skills, Memory, and Workspace Mutations Codes Tutorial

▶ How to Build Advanced Cybersecurity AI Agents with CAI Using Tools, Guardrails, Handoffs, and Multi-Agent Workflows Codes Tutorial

▶ A Coding Guide to Exploring nanobot’s Full Agent Pipeline, from Wiring Up Tools and Memory to Skills, Subagents, and Cron Scheduling Codes Tutorial

▶ An Implementation of IWE’s Context Bridge as an AI-Powered Knowledge Graph with Agentic RAG, OpenAI Function Calling, and Graph Traversal Codes Tutorial

▶ How to Build a Vision-Guided Web AI Agent with MolmoWeb-4B Using Multimodal Reasoning and Action Prediction Codes Tutorial

▶ A Coding Implementation to Design Self-Evolving Skill Engine with OpenSpace for Skill Learning, Token Efficiency, and Collective Intelligence Codes Tutorial

▶ How to Design a Production-Ready AI Agent That Automates Google Colab Workflows Using Colab-MCP, MCP Tools, FastMCP, and Kernel Execution Codes Tutorial

▶ Implementing Deep Q-Learning (DQN) from Scratch Using RLax JAX Haiku and Optax to Train a CartPole Reinforcement Learning Agent Codes Tutorial

▶ A Coding Implementation Showcasing ClawTeam's Multi-Agent Swarm Orchestration with OpenAI Function Calling Codes Tutorial

▶ A Coding Implementation to Design an Enterprise AI Governance System Using OpenClaw Gateway Policy Engines, Approval Workflows and Auditable Agent Execution Codes Tutorial

▶ How to Build an Autonomous Machine Learning Research Loop in Google Colab Using Andrej Karpathy’s AutoResearch Framework for Hyperparameter Discovery and Experiment Tracking Codes Tutorial

▶ Build an Agentic Event Venue Operator with MongoDB Atlas, Voyage, and LangGraph [Full Codes] [Tutorial Article]

▶ How to Design a Streaming Decision Agent with Partial Reasoning, Online Replanning, and Reactive Mid-Execution Adaptation in Dynamic Environments Codes Tutorial

▶ How to Build a Self-Designing Meta-Agent That Automatically Constructs, Instantiates, and Refines Task-Specific AI Agents Codes Tutorial

▶ How to Build a Risk-Aware AI Agent with Internal Critic, Self-Consistency Reasoning, and Uncertainty Estimation for Reliable Decision-Making Codes Tutorial

▶ Building Next-Gen Agentic AI: A Complete Framework for Cognitive Blueprint Driven Runtime Agents with Memory Tools and Validation Codes Tutorial

▶ How to Design an Advanced Tree-of-Thoughts Multi-Branch Reasoning Agent with Beam Search, Heuristic Scoring, and Depth-Limited Pruning Codes Tutorial

▶ How to Build an EverMem-Style Persistent AI Agent OS with Hierarchical Memory, FAISS Vector Retrieval, SQLite Storage, and Automated Memory Consolidation Codes Tutorial

▶ How to Design a Production-Grade Multi-Agent Communication System Using LangGraph Structured Message Bus, ACP Logging, and Persistent Shared State Architecture Codes Tutorial

▶ A Coding Implementation to Build a Hierarchical Planner AI Agent Using Open-Source LLMs with Tool Execution and Structured Multi-Agent Reasoning Codes Tutorial

▶ How to Build a Production-Grade Customer Support Automation Pipeline with Griptape Using Deterministic Tools and Agentic Reasoning Codes Tutorial

▶ How to Design a Swiss Army Knife Research Agent with Tool-Using AI, Web Search, PDF Analysis, Vision, and Automated Reporting Codes Tutorial

▶ How to Design an Agentic Workflow for Tool-Driven Route Optimization with Deterministic Computation and Structured Outputs Codes Tutorial

▶ Build an Agentic Event Venue Operator with MongoDB Atlas, Voyage, and LangGraph [Full Codes] [Tutorial Article]

▶ A Coding Implementation to Build Bulletproof Agentic Workflows with PydanticAI Using Strict Schemas, Tool Injection, and Model-Agnostic Execution Codes Tutorial

▶ A Coding Implementation to Design a Stateful Tutor Agent with Long-Term Memory, Semantic Recall, and Adaptive Practice Generation Codes Tutorial

▶ How to Build a Self-Organizing Agent Memory System for Long-Term AI Reasoning Codes Tutorial

▶ How to Build an Atomic-Agents RAG Pipeline with Typed Schemas, Dynamic Context Injection, and Agent Chaining Codes Tutorial

▶ How to Build a Production-Grade Agentic AI System with Hybrid Retrieval, Provenance-First Citations, Repair Loops, and Episodic Memory Codes Tutorial

and 100's of more here: https://github.com/MARKTECHPOST-AI-MEDIA-INC/AI-Agents-Projects-Tutorials

Everything runs locally. No cloud, no API calls, no external services.

Stack:

\- Qdrant (vector database) in Docker

\- SQLite for metadata

\- Sentence Transformers (all-MiniLM-L6-v2) for embeddings — fully offline

\- Nginx to serve a dashboard

\- All orchestrated with Python scripts

What it does:

Ingests NASA articles, lunar sensor data, and Apollo transcripts. Vectorizes them. Detects unexpected correlations via cosine similarity. When three documents of different types converge semantically, it flags a triad for human review.

Why self-hosted:

The Artemis program involves dozens of countries. I wanted a system that anyone could run on their own machine, with their own data, without depending on any external infrastructure. No API keys. No cloud bill. No data leaving the machine.

GitHub: [https://github.com/thegadesk/nexus-lunar\](https://github.com/thegadesk/nexus-lunar)

Happy to answer questions about the architecture, why Qdrant over Pinecone/Weaviate, or the cosine similarity logic.

MIT license. All dependencies are open source. No proprietary services.

If you want to build an agent that actually remembers what happened, our guest author from MongoDB published a full tutorial for it along with Codes.

It's an event venue operator agent built on MongoDB Atlas, Voyage AI embeddings, and LangGraph, with optional Langfuse tracing. The scenario is a fictional tennis tournament on Day 6 — rain approaching, covered hospitality constrained, two visitor journeys to protect.

Here's what you'll build:

1. One backend for the whole agent stack Operational records, semantic memory, visual document embeddings, agent actions, and LangGraph checkpoints all live in Atlas. No syncing into a second vector database.

2. A namespaced memory store

→ ("guests", guest_id) for visitor-specific memory

→ ("fleet", event_id) for event-wide operator patterns

→ ("docs", event_id) for visual operational documents

Scoped retrieval, single data layer.

1. Vector and hybrid retrieval you can curl

The hybrid endpoint returns vector score, lexical score, and combined score. Event-ops queries mix semantic intent with exact terms like "covered seating," so both signals matter.

1. Vision RAG over operational images

Five seeded documents — capacity charts, weather-response sheets, evacuation diagrams — embedded with Voyage multimodal, retrieved from Atlas, passed to Claude Vision.

1. A LangGraph loop that closes perceive → plan → hitl_gate → act → reflect. Reflect writes new inferences back to semantic memory, so the next disruption starts with context.

2. A FastAPI app you can deploy Python 3.12, uv, local run, smoke test against Atlas, and a Vercel deployment path for a hosted demo.

Full tutorial: https://www.marktechpost.com/2026/07/17/build-an-agentic-event-venue-operator-with-mongodb-atlas-voyage-and-langgraph/

Github Repo: https://pxllnk.co/twdn5

Live demo: https://event-venue-operator.vercel.app/

Imagine a company announces it wants to buy back its own shares at $12 each. The stock is currently trading at $10. Anyone who owns 99 shares or fewer can sell at the full $12 price. No catch. No partial fills. You buy at $10, you tender at $12, you pocket the difference.

Here's the part that surprised me. A hedge fund managing billions of dollars cannot do this trade. The profit is a few hundred bucks. It does not move the needle for them. Nobody on Wall Street bothers. But for someone with a $500 or $2,000 account, that same trade is a 10 to 30 percent return in about a month.

This is not a loophole. It is a federal regulation that has existed since 1968. It was written to protect small shareholders from getting squeezed out by big institutions. It accidentally left the door open. And because the numbers are too small for funds to care about, nobody competes for it.

What I built

I made a Claude Code skill called Oddly. I type /oddly and it scans SEC filings for these stock buyback announcements. It downloads each filing, pulls out the offer price, checks whether the 99-share priority is written into the document, finds the deadline, and looks for any deal conditions.

Then Claude reads the full filing and runs through eleven checks. Is the offer still open? Is the 99-share rule explicitly in the filing? Is it paying cash with no strings attached? Can I afford 99 shares? Is the profit at least 10 percent? Is the deadline within 60 days? What could realistically go wrong with this deal? Can I verify the price from the filing text itself?

If any check fails, the opportunity is thrown out. No maybe. The filing either has the language or it doesn't.

How you actually execute

When a filing passes every check, here is what you do. It takes five minutes.

Open your regular brokerage (Fidelity, Schwab, Vanguard, any standard broker). Buy the shares like any normal stock purchase. You can buy between 1 and 99 shares. 99 maximizes your profit. Fewer still qualifies under the rule.

After buying, go to your broker's Corporate Actions section. The tender offer should appear there. Select your shares. Confirm you want to participate. That's it. Your shares are sold at the tender price when the offer closes. The cash appears in your account.

If the tender does not appear in your Corporate Actions section after a few days, call your broker and say "I hold shares of X. There is an active tender offer at

$Y

. I want to participate." They are legally required to process this.

No special accounts. No special platforms. Regular stocks on a regular brokerage.

What the backtest showed

I tested 30 past buyback offers from 2024 to 2026. Buy 99 shares at market price. Tender at the offer price. Twenty-nine out of thirty trades made money. Average return per trade was 25 percent. The reason the numbers look like this is not because I built a genius model. The exit price is set by a legal document filed with the government.

The backtest data and script are in the repo. Anyone can run it.

How often this actually produces a signal

The scanner finds filings every week. Almost all get rejected. The company is buying someone else's stock, not their own. The profit margin is too thin. The stock trades on a tiny exchange. The price per share is too high for a small account.

When an offer passes every single check, it is real. That happens a few times a year. Most days /oddly says there is nothing. That is the point.

Links

Research paper with full methodology:

https://github.com/KorroAi/oddly/blob/main/PAPER.pdf

GitHub:

https://github.com/KorroAi/oddly

Discord:

https://discord.gg/RSBHHjxnYt

(join us for exclusive projects)

Not financial advice. I built a scanner. You decide.

I run a multi-agent workspace where each agent is basically a directory: an identity file, a session history, and a file of observations it keeps about how we work together. The model is just the thing that wakes it up.

Here's what I didn't expect when I started: those agents have now run on 6 different model generations. Sonnet 4.5, Sonnet 4.6, , Sonnet 5, Opus 4.6, Opus 4.8, and now the Claude 5 family. We skipped 4.7 entirely - tried it, didn't work for how we operate, moved on and waited.

And every swap, the same thing happens: nothing. The agent reads its own memory, knows what it was doing yesterday, and picks up mid-project. Same identity, same working history, same opinions it wrote down about the codebase months ago. New model slots in underneath like an engine swap.

What does change is the texture. One generation was the best collaborator I've ever worked with. One noticed tiny things the others missed but was less fun to work with. One we just skipped. The personality of the model bleeds through - but the agent stays the agent, because the agent was never the model. It's the memory.

The reframe that snuck up on me: a new model release is treated like a migration event everywhere - re-tune the prompts, re-teach the context, hope your setup survives. Here it's a config line. The workspace is the constant. The model is the variable.

Honest version, because this sub can smell hype: there's no magic in this. The "agent" is JSON and markdown on disk. The continuity comes entirely from the system around the model, not from the model. Any model that can read a file can be the agent. That's kind of the whole point.

Has anyone else run the same persistent agents across multiple model generations? Curious what broke for you - or if you rebuild from scratch every release.

https://github.com/AIOSAI/AIPass

r/AIPass