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Aaron Spooner reposted thisAaron Spooner reposted thisEverything works perfectly… until the agent makes one wrong move. That’s when you realize intelligence without control is just risk. Agentic AI isn’t about giving systems total freedom, it’s about giving them safe boundaries so they can act reliably in the real world. Here’s a simple breakdown of the 7 control mechanisms every production agent needs: 1. Human-in-the-Loop Critical decisions still pass through humans. High-impact actions like payments, deletions, or irreversible changes always require approval. 2. Approval Gates Structured checkpoints stop agents from moving ahead blindly. Useful in multi-step workflows where conditions must be validated first. 3. Budget Limits Agents operate within strict token, API, and compute caps. Prevents runaway loops, unexpected bills, and uncontrolled tool usage. 4. Timeouts Every action has a maximum execution window. If an agent stalls, keeps retrying, or waits too long, it safely stops and falls back. 5. Confidence Thresholds Agents only act when they’re confident enough. Low-confidence outputs trigger escalation instead of hallucinated decisions. 6. Rollback Logic If something fails, the system can undo it. Reversible steps and checkpoints ensure mistakes don’t corrupt data or workflows. 7. Audit Trails Every decision is logged and traceable. This creates transparency, trust, and easier debugging across the entire agent workflow. Strong agents aren’t the ones that think the most, they’re the ones that operate inside the right boundaries.
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Aaron Spooner reposted thisAaron Spooner reposted thisConcepts every developer should know: CI/CD pipeline. A CI/CD pipeline is an automated workflow that facilitates continuous integration (CI) and continuous delivery or deployment (CD) by managing code building, testing, and release processes. It integrates the various stages of the software development lifecycle (SDLC) into a seamless, repeatable process. Continuous ‘delivery’ and ‘deployment’ are sometimes used synonymously. But there is a clear and important distinction between the two. Delivery is about ensuring the software can be released at any time. It requires manual intervention to deploy to production. Deployment, on the other hand, does the release through automated workflows. Tomorrow, we'll release a detailed article (with visuals) on CI/CD pipelines in our newsletter. Don't want to miss out? Join 27,501+ engineers: https://lnkd.in/gyJq8WS7 What else would you add? -- ♻️ Save & repost to help others learn system design. ➕ Follow Nikki Siapno + turn on notifications.
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Aaron Spooner reposted thisAaron Spooner reposted thisLangchain is great, you just need to follow its documentation… If you are an AI Engineer I’m sure you have suffered this before. LangChain has gained lot of popularity for prototyping agents. But when you move into production… reality hits hard. Here’s what I’ve seen again and again with devs trying to ship LangChain-powered agents: 🔹 Too many abstractions Agent → AgentExecutor → ZeroShotAgent → ConversationalAgent. Great for slides, terrible for debugging. Developers spend hours digging through black boxes to find why a prompt broke. 🔹 Performance & cost overhead Cold starts in serverless = painful. Extra layers = extra latency. One RAG query costs 2.7× more tokens with LangChain vs manual implementation. At scale, that’s $$$. 🔹 Memory & resource headaches From leaks to 10GB RAM consumption on simple RAG systems. Cleanup patterns? Barely documented. 🔹 Breaking changes every week ~67% of “minor updates” break something. Teams spend more time chasing updates than building features. 🔹 Production ≠ Documentation Almost 90% of production apps avoid official LangChain patterns. Built-in memory, LCEL, LangGraph? Most teams roll their own. 👉 That’s why the smartest AI engineers now treat LangChain as a utility library and build orchestration themselves. Others are moving to other toolings as well… Bottom line: LangChain is amazing for prototyping, but production at scale is a different game! #ai #agents #langchain
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Aaron Spooner reposted thisAaron Spooner reposted thisThese YouTube channels got you covered if you’d like to start learning AI Learning Artificial Intelligence can feel confusing, as there are so many concepts to grasp, starting with math foundations to deploying successfully. But with the right channels, you can learn everything step by step, for free. Here’s a cheat sheet highlighting the best YouTube channels for AI in 2025, organized into categories like Math, Python, Core ML, Deep Learning, MLOps, and Research. Each channel simplifies tough concepts, shows practical coding examples, and keeps you updated with the latest AI breakthroughs. ✅ Channels Covered: 1. Math for ML → Essence of Linear Algebra (3Blue1Brown), Khan Academy, StatQuest with Josh Starmer. 2. Python for ML → Programming with Mosh, Tech with Tim, Sentdex, freeCodeCamp.org. 3. Core ML Fundamentals → Stanford CS229, Andrej Karpathy, CodeEmporium, Corey Schafer. 4. MLOps & Deployment → Krish Naik, CodeBasics, Cloud With Raj, MLOps Community. 5. Projects & Kaggle → Abhishek Thakur, Data Professor, CodeBasics (projects), Krish Naik (end-to-end ML). 6. Deep Learning → Andrej Karpathy, freeCodeCamp.org, DeepLearningAI, Two Minute Papers. 7. Staying Updated (Research) → Arxiv Insights, The Cutting Edge School, Two Minute Papers, Yannic Kilcher. Feel free to save this guide and share with others who have similar interests. #AI
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Aaron Spooner reposted thisAaron Spooner reposted this🚀 𝗚𝗲𝗻𝗔𝗜 𝗶𝘀 𝗻𝗼𝘁 𝗵𝘆𝗽𝗲 𝗮𝗻𝘆𝗺𝗼𝗿𝗲. 𝗜𝘁’𝘀 𝗥𝗢𝗜. Google just released a global report on the business impact of Generative AI, and the numbers speak louder than any keynote. 🔑 Key findings 🔸74% of enterprises using GenAI report ROI in the first year. 🔸86% of those seeing revenue growth report +6% or more in annual revenue. 🔸84% can move from idea → production in under 6 months, cutting innovation cycles drastically. 🔸Productivity is skyrocketing: 45% of orgs say employee output has at least doubled. 🔸Better experiences: 85% report higher engagement & satisfaction thanks to GenAI. 🔸Security, too: 56% improved their ability to detect threats and reduce response time. But the real differentiator? 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽. 👉 Organizations with strong C-suite sponsorship are 91% more likely to report major revenue gains. 💡 𝗧𝗵𝗲 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆 𝗶𝘀 𝗰𝗹𝗲𝗮𝗿: 𝗚𝗲𝗻𝗔𝗜 𝗶𝘀𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗮 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗽𝗹𝗮𝘆—𝗶𝘁’𝘀 𝗮 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗲𝗻𝗴𝗶𝗻𝗲. The winners are not the ones “experimenting,” but those embedding GenAI into strategy, operations, and culture. 📌 Full report here 👇 Are you measuring the ROI of GenAI in your organization yet—or still stuck in “pilot purgatory”? #ai #google #genai
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Aaron Spooner reposted thisAaron Spooner reposted this7 key LLM generation parameters that every AI engineer must know: . . If you're struggling with inconsistent or bland LLM outputs? It's often just a matter of tuning the right parameters. Mastering them lets you craft sharper, more reliable results without endless prompt tweaks. The visual below highlights the 7 that matter most. Let's break them down one by one! 1️⃣ Max tokens This is a hard cap on how many tokens the model can generate in one response. - Too low → truncated outputs - Too high → could lead to wasted compute. 2️⃣ Temperature This parameter governs randomness. - Low temperature (~0) makes the model deterministic. - Higher temperature boosts creativity, diversity, but also noise. Typically, you would prefer to keep it low for QA/chatbots, higher for creative tasks. 3️⃣ Top-k The default way to generate the next token is to sample from all tokens, proportional to their probability. This parameter restricts sampling to the top k most probable tokens. This enforces focus, but overly small k may give repetitive outputs. 4️⃣ Top-p Instead of picking from all tokens or top k tokens, the model samples from a probability mass up to p. Example: top_p=0.9 → only the smallest set of tokens covering 90% probability are considered. So it is more adaptive than top_k. 5️⃣ Frequency penalty This parameter penalizes reusing tokens that have already appeared frequently. Positive values reduce repetition, negative values exaggerate it. Useful for summarization (avoid redundancy) or poetry (intentional repetition). 6️⃣ Presence penalty This parameter encourages the model to bring in new tokens not yet seen in the text. Higher values push for novelty, lower values make the model stick to known patterns. Handy for exploratory generation where diversity of ideas is valued. 7️⃣ Stop This parameter declares a custom list of tokens that immediately halt generation. Critical in structured outputs (e.g., JSON), preventing spillover text. Let’s you enforce strict response boundaries without heavy prompt engineering. ____ That's a wrap! 👉 Interested in ML/AI Engineering? Sign up for our newsletter for in-depth lessons and get a FREE AI Agents Guidebook with 12 hands-on projects: link in first comment. ____ Share this with your network if you found this insightful ♻️ Follow me (Akshay Pachaar) for more insights and tutorials on AI and Machine Learning!
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Aaron Spooner reposted thisAaron Spooner reposted thisHere's something worth your time. Google went out and actually asked 3,500 executives a simple question: where is AI making you money? 🤔 The results landed in this fascinating and huge 48-page report linked down below. Whether you're running things from on high or getting your hands dirty with the code, this is about what's working in AI right now. A few pages that'll save you some digging: ➡️ P16: breaks down which industries are using AI for what. ➡️ P26: How people are actually working differently. ➡️ P31 shows the ROI numbers. ➡️ P41 covers where the smart money is going next. The whole thing is worth reading if you're trying to separate signal from noise in this space. One mystery: whoever designed the graphics went with these abstract space coral-looking things. They're beautiful, but I have no idea what they're supposed to be. Sometimes the best things come wrapped in the strangest packages. 😂 #google #lifeatgoogle #googlecloud #ai #data #research
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Aaron Spooner reposted thisAaron Spooner reposted thisNow you can build a working AI agent from a single screenshot 😱 You can basically copy any screenshot you have of n8n workflow into your workspace. Here’s how: Step 1 – Grab any screenshot Find a YouTube video, a diagram, or an agent workflow you want to replicate. Take a clean screenshot. Step 2 – Feed it to Claude - Upload that screenshot to Claude. - Give it a prompt to replicate the steps from the agent. Step 3 – Spin it up in n8n - Install n8n Chat extension - Paste Claude’s output And watch it generate the workflow for you. Run, test, tweak. Pro tip: Add soft fail paths for API limits and log every step for easy debug. —— And that’s it… if you need the claude prompt lmk 🙌🏻
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Aaron Spooner shared this"You look like you'd be very good at basketball!" "Which sports do you play?" "Why do you always act so white?" Do not buy into the stereotypes. If you have a dream, pursue it no matter what people tell you. You know what you want. You know what you have to do to achieve it. Stay on track Because you know it will all be worth it in the end. In 9 days, I begin employment as a NASA Pathways Engineering Intern #NASAintern #BlackExcellence #NASA #UCF
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Aaron Spooner liked thisAaron Spooner liked thisAI users are working longer because, for the first time, our tools can finally keep pace with our imagination. But speed doesn't matter if you lack a destination. When output is cheap, your greatest leverage isn't how much you build, but the taste to know what is worth building - and the rest required to see the difference. #ai #programming #softwareengineering
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Aaron Spooner liked thisAaron Spooner liked thisFrom "Raw" streams to "Refined" insights! Explore the 9 essential design patterns every data engineer must master. 𝗜𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 Highway, local road, and service lane. → Batch = scheduled trucks arriving at night with bulk goods. → Streaming = live traffic flowing constantly through highways. → CDC = only sending changed parcels instead of whole trucks. 𝘊𝘩𝘰𝘰𝘴𝘦 𝘣𝘢𝘴𝘦𝘥 𝘰𝘯 𝘭𝘢𝘵𝘦𝘯𝘤𝘺 𝘯𝘦𝘦𝘥𝘴, 𝘯𝘰𝘵 𝘩𝘺𝘱𝘦. • 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 Warehouse, open yard, and hybrid mall. → Data lake = huge yard storing everything as‑is. → Warehouse = neatly labeled shelves for fast picking. → Lakehouse = yard + warehouse with rules and performance. 𝘚𝘵𝘰𝘳𝘢𝘨𝘦 𝘤𝘩𝘰𝘪𝘤𝘦 𝘥𝘦𝘧𝘪𝘯𝘦𝘴 𝘥𝘰𝘸𝘯𝘴𝘵𝘳𝘦𝘢𝘮 𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦 𝘢𝘯𝘥 𝘤𝘰𝘴𝘵. • 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 Kitchen prep before vs after storage. → ETL = wash and cut vegetables before refrigerating. → ELT = store groceries first, prep when needed. → Incremental = only recook what actually changed today. 𝘌𝘓𝘛 𝘧𝘰𝘳 𝘤𝘭𝘰𝘶𝘥 𝘴𝘤𝘢𝘭𝘦, 𝘌𝘛𝘓 𝘧𝘰𝘳 𝘤𝘰𝘮𝘱𝘭𝘪𝘢𝘯𝘤𝘦-𝘩𝘦𝘢𝘷𝘺 𝘥𝘰𝘮𝘢𝘪𝘯𝘴. • 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 Factory manager and sensors. → DAGs = manager’s checklist: step‑by‑step run order. → Event‑driven = machines start when a sensor is triggered. 𝘌𝘷𝘦𝘯𝘵-𝘥𝘳𝘪𝘷𝘦𝘯 𝘥𝘦𝘤𝘰𝘶𝘱𝘭𝘦𝘴 𝘴𝘺𝘴𝘵𝘦𝘮𝘴, 𝘋𝘈𝘎𝘴 𝘦𝘯𝘴𝘶𝘳𝘦 𝘰𝘳𝘥𝘦𝘳. • 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 Crash‑safe roads. → Idempotent jobs = same route, same result, even if retried. → Retries + dead‑letter queues = tow truck and parking yard. → Backfills = replaying yesterday’s route to fix delivery errors. 𝘋𝘦𝘴𝘪𝘨𝘯 𝘧𝘰𝘳 𝘧𝘢𝘪𝘭𝘶𝘳𝘦 𝘧𝘳𝘰𝘮 𝘥𝘢𝘺 𝘰𝘯𝘦. • 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 & 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 Building codes and inspectors. → Validation = safety checks before people move in. → Schema evolution = renovating without collapsing the building. → Lineage = city map showing every road from source to square. 𝘎𝘰𝘷𝘦𝘳𝘯𝘢𝘯𝘤𝘦 𝘦𝘯𝘢𝘣𝘭𝘦𝘴 𝘵𝘳𝘶𝘴𝘵, 𝘯𝘰𝘵 𝘣𝘶𝘳𝘦𝘢𝘶𝘤𝘳𝘢𝘤𝘺. • 𝗦𝗲𝗿𝘃𝗶𝗻𝗴 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 Front desks and ticket counters. → Semantic layer = one menu of official dish names and prices. → APIs = ticket windows giving controlled, metered access. 𝘏𝘰𝘸 𝘥𝘢𝘵𝘢 𝘪𝘴 𝘤𝘰𝘯𝘴𝘶𝘮𝘦𝘥 𝘮𝘢𝘵𝘵𝘦𝘳𝘴 𝘢𝘴 𝘮𝘶𝘤𝘩 𝘢𝘴 𝘱𝘳𝘰𝘤𝘦𝘴𝘴𝘪𝘯𝘨. • 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 Express lanes and parking zones. → Partitioning = dedicated lanes for different destinations. → Caching = small local store so you skip the long supermarket. → Tiered storage + on‑demand compute = pay only for busy hours. 𝘖𝘱𝘵𝘪𝘮𝘪𝘻𝘦 𝘲𝘶𝘦𝘳𝘪𝘦𝘴, 𝘯𝘰𝘵 𝘫𝘶𝘴𝘵 𝘪𝘯𝘧𝘳𝘢𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦. Illustration Credits: Shalini Goyal Stay tuned for more such data engineering concepts and analogies.😉
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Education
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LSU
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Presented topics:
“Generalized Functions and Applications within Advanced Electrodynamics” Louisiana State University, Baton Rouge-2012.
“Understanding the Properties of Fused Silica Fibers for Virgo’s Current Gravitational Wave Interferometer Mirror Suspensions” at Louisiana State University, Baton Rouge-2011 and University of Rome “Sapienza” in Rome, Italy and Paris, France-2011.
Licenses & Certifications
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Secondary Education - Physics & Physical Science
Louisiana State University
Issued
Courses
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Computational Physics (C++)
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Introduction to Programming for Engineers (Matlab)
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Oracle BI 11g R1: Create Analyses and Dashboards
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Oracle Database: Introduction to SQL
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Oracle Java Programming
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Quantum Mechanics
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Languages
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English
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French
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1 person has recommended Aaron
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