Lamhot Siagian

Today I configured Hermes Agent, Telegram, and Ollama to run locally. Follow AI Engineering Insider What does that mean? You can build your own local AI automation agent that can: ✅ Send daily updates ✅ Automate repeated tasks ✅ Schedule reminders ✅ Run locally with Ollama ✅ Connect to Telegram as your personal AI assistant This is powerful because you do not always need a complex SaaS tool to automate simple workflows. Sometimes, the best AI agent is the one running on your own machine. I’m preparing a full step-by-step guide. Comment “GUIDE” if you want it.

Multi-User LLM Agents Every agent framework assumes one user giving instructions. But deploy an agent into a team workflow, and suddenly it has multiple bosses with conflicting goals, private information, and different authority levels. This work formalizes multi-user interaction as a multi-principal decision problem and introduces Muses-Bench with three scenarios: instruction following under authority conflicts, cross-user access control, and multi-user meeting coordination. Even the best model, Gemini-3-Pro, only averages 85.6% across tasks. On meeting coordination, no model exceeds 64.8% success rate. Privacy-utility tradeoffs are especially brutal: models that score near-perfect on privacy (Grok-3-Mini at 99.6%) tank on utility (60.1%). Why does it matter? As agents move into organizational tools, Slack bots, and shared workspaces, multi-principal conflicts become the default, not the exception. Current models aren't ready. They leak more privacy over multi-turn interactions and can't maintain stable prioritization under conflicting objectives. Paper: arxiv.org/abs/2604.08567 Take free Agentic AI Certification: aiengineeringinsider.com/certification ♻ Repost if you found this useful.

List of Free GenAI Courses LangChain for LLM Application Development by Deeplearning.AI LLMOps by DeepLearning.AI Automated Testing for LLMOps by DeepLearning.AI Building Generative AI Applications Using Amazon Bedrock by AWS Efficiently Serving LLMs by DeepLearning.AI Building Systems with the ChatGPT API by DeepLearning.AI Serverless LLM apps with Amazon Bedrock by DeepLearning.AI Building Applications with Vector Databases by DeepLearning.AI Automated Testing for LLMOps by DeepLearning.AI Build LLM Apps with LangChain.js by DeepLearning.AI Advanced Retrieval for AI with Chroma by DeepLearning.AI Operationalizing LLMs on Azure by Coursera Generative AI Full Course – Gemini Pro, OpenAI, Llama, Langchain, Pinecone, Vector Databases & More by freeCodeCamp.org Training & Fine-Tuning LLMs for Production by Activeloop Prompt Engineering, RAG and Fine-Tuning LangChain & Vector Databases in Production by Activeloop Reinforcement Learning from Human Feedback by DeepLearning.AI Building Applications with Vector Databases by DeepLearning.AI Finetuning Large Language Models by Deeplearning.AI LangChain: Chat with Your Data by Deeplearning.AI Building Systems with the ChatGPT API by Deeplearning.AI Prompt Engineering with Llama 2 by Deeplearning.AI Building Applications with Vector Databases by Deeplearning.AI ChatGPT Prompt Engineering for Developers by Deeplearning.AI Advanced RAG Orchestration series by LlamaIndex Prompt Engineering Specialization by Coursera Augment your LLM Using Retrieval Augmented Generation by Nvidia Knowledge Graphs for RAG by Deeplearning.AI Open Source Models with Hugging Face by Deeplearning.AI Vector Databases: from Embeddings to Applications by Deeplearning.AI Understanding and Applying Text Embeddings by Deeplearning.AI JavaScript RAG Web Apps with LlamaIndex by Deeplearning.AI Quantization Fundamentals with Hugging Face by Deeplearning.AI Preprocessing Unstructured Data for LLM Applications by Deeplearning.AI Retrieval Augmented Generation for Production with LangChain & LlamaIndex by Activeloop Quantization in Depth by Deeplearning.AI Evaluation Building and Evaluating Advanced RAG Applications by DeepLearning.AI Evaluating and Debugging Generative AI Models Using Weights and Biases by Deeplearning.AI Quality and Safety for LLM Applications by Deeplearning.AI Red Teaming LLM Applications by Deeplearning.AI Multimodal How Diffusion Models Work by DeepLearning.AI How to Use Midjourney, AI Art and ChatGPT to Create an Amazing Website by Brad Hussey Build AI Apps with ChatGPT, DALL-E and GPT-4 by Scrimba 11-777: Multimodal Machine Learning by Carnegie Mellon University Prompt Engineering for Vision Models by Deeplearning.AI Agents Building RAG Agents with LLMs by Nvidia Functions, Tools and Agents with LangChain by Deeplearning.AI AI Agents in LangGraph by Deeplearning.AI AI Agentic Design Patterns with AutoGen by Deeplearning.AI Multi AI Agent Systems with crewAI by Deeplearning.AI Building Agentic RAG with LlamaIndex by Deeplearning.AI LLM Observability: Agents, Tools, and Chains by Arize AI Building Agentic RAG with LlamaIndex by Deeplearning.AI Agents Tools & Function Calling with Amazon Bedrock (How-to) by AWS Developers ChatGPT & Zapier: Agentic AI for Everyone by Coursera Multi-Agent Systems with AutoGen by Victor Dibia [Book] Large Language Model Agents MOOC, Fall 2024 by Dawn Song & Xinyun Chen – A comprehensive course covering foundational and advanced topics on LLM agents. CS294/194-196 Large Language Model Agents by UC Berkeley

Want to know if you’re actually ready for Agentic AI interviews? Take the Agentic AI Foundations Certification Exam for free. 👉 AI Engineering Insider Lamhot Siagian Start here: 👉 aiengineeringinsider.com/certification This practice exam helps you test and strengthen your knowledge of: LLMs Agents RAG Tool use Memory Evaluation AI safety Earn the Certified Agentic AI Foundations Associate credential and use it as a structured way to review the fundamentals before your next AI interview.

I spent this week building and launching AI Engineering Insider. Website is now live: 👉 aiengineeringinsider.com I built it using Next.js and Firebase as the foundation. The goal is simple: Create one practical platform for engineers who want to learn AI Engineering, prepare for modern AI interviews, and understand how production AI systems are actually built. AI is moving fast, but most learning resources are scattered across courses, GitHub repos, papers, newsletters, and random social posts. I wanted to build something more focused. AI Engineering Insider will cover: ✅ AI Engineer interview prep ✅ Agentic AI systems ✅ RAG architecture ✅ LLM evaluation and testing ✅ AI system design ✅ Forward Deployed AI Engineer roles ✅ AI Product Manager resources ✅ Production AI engineering skills ✅ Premium guides, reports, and newsletters This is just the first version. I will keep improving the website, adding more guides, free resources, technical posts, and practical learning materials for engineers who want to stay relevant in the AI era. More coming soon. Newsletter: aiengineeringinsider.substack.com/subscribe Page: linkedin.com/company/ai-eng… #AIEngineering #NextJS #Firebase #AgenticAI #RAG #LLM #AIEngineer #SystemDesign #BuildInPublic

A former MIT researcher just mapped the path from a worm to a digital human brain. The plan scales from a 302-neuron worm to 86 billion neurons. Three technologies are making this tractable. 1. High-resolution imaging now maps neurons at scale 2. Functional scans capture whole brains in young fish 3. Biologically accurate neuron models can run on GPUs Connectomics cost dropped from $16,500 per neuron to $100. A complete fruit fly brain with 140,000 neurons has already been reconstructed. Rough estimates suggest simulating a human brain needs 600 exaFLOP/s of compute. That is roughly 50,000 H100 chips. One major AI lab already runs over 200,000. The real bottleneck is data. Hundreds of next-gen microscopes must run for years to stain receptors and map connectivity. Early worm and fish emulations are already live. Paper: pdf.isaak.net/scaling-emulat…

Harness engineering is the new must-have skill. This free course teaches it. Coding agents can write code. They just can't reliably finish real tasks across sessions without breaking things. A new open-source course called Learn Harness Engineering tackles this head-on. The "harness" is the workbench around the agent. Not the model itself. It breaks reliability into five mechanisms: instructions, state, validation, scope, and sessions. Without it, your agent forgets context, redoes work, and declares victory before tests pass. With one, every task becomes trackable, resumable, and verifiable. The curriculum ships 12 theory lessons and 6 hands-on projects, all built around one evolving desktop app. Each project's output feeds directly into the next. You also get reusable templates: > AGENTS.md instruction files > feature_list.json for scope > init.sh for environment setup > Progress logs for continuity Drop them into any repo to stabilize Claude Code or Codex immediately. Repo: github.com/walkinglabs/le…

Browser Use just killed every browser automation framework in 592 lines. It's called Browser Harness, a Python tool that rethinks how AI controls a browser. Traditional setups like Selenium or Playwright wrap Chrome in rigid APIs. The agent can only do what the framework allows. This one removes the rulebook entirely. Built in 592 lines, it connects straight to Chrome through a raw protocol. One websocket, zero middleman. The wild part is self-healing. If the agent needs a function that doesn't exist, it edits helpers.py mid-task and keeps going. Missing an upload feature? It writes one. Task finishes. Why it matters for anyone building agents: > No framework locking capabilities > Direct Chrome control via CDP > Drops into Claude Code instantly > Agent extends its own toolkit Repo: github.com/browser-use/br…

Repo: github.com/anombyte93/prd…

You can now run deep research on your laptop without OpenAI. Most AI research tools send your queries to the cloud. Your data leaves the machine the moment you hit search. Local Deep Research flips that model entirely. It's an open-source assistant that runs fully on your device. Ask a complex question and it searches the web, academic papers, and your private documents at once. The system synthesizes everything into a cited report. It hits ~95% on the SimpleQA benchmark.

A few signals from the 100-role sample: 47 roles mentioned agent/agentic/multi-agent systems 33 mentioned RAG or GraphRAG 23 mentioned Python 19 mentioned LangChain/LangGraph/LlamaIndex 12 mentioned evals 22 had cloud/platform signals

The market is hiring for engineers who can connect LLMs to: Business data APIs Tools Workflows Cloud systems Evaluation Security Governance That is very different from "I built a chatbot."

The AI Engineer job market has moved past prompt writing. I reviewed 100 public AI engineering roles from 100 companies for the May 2026 AI Engineer Job Market Report. The signal is clear: Companies want engineers who can connect LLMs to data, tools, workflows, evals, cloud, and governance. Not demos. Production systems. Free to download: shop.beacons.ai/aiengineeringi…

I want to connect with more people who are into: 1. Data Engineering, Analytics Engineering, Data Science, Data Analysis, BI, etc 2. AI & Technology 3. Content creation Please x, do your magic!

I want to connect with more people who are into: 1. Entrepreneurship 2. AI & Technology 3. Flow Let’s grow together!

Vibe coding isn’t an avoidance skill. It’s multiplying it. The best builders know what to build, ship fast, solve business problems, and own the outcome. The game was never typing code. Subscribe: aiengineeringinsider.substack.com/subscribe

@_vmlops This ebook is included for free with the AI Engineering Insider monthly subscription, where you can access all premium ebooks and future guides: aiengineeringinsider.substack.com/subscribe Or, if you prefer to get only this individual guide, you can check it out here: shop.beacons.ai/aiengineeringi…

Cracking the Forward Deployed AI Engineer Interview Roadmap, System Design, Case Studies & Mock Interviews for FDE Roles in 2026 drive.google.com/file/d/1HUCOIX…

DSA Coding Interview Playbook Include brute force and best approach solution link: drive.google.com/file/d/1dFOEzz…