Artificial Intelligence

Author(s): Aarush Mahajan Originally published on Towards AI. Navigating the Invisible Hazard: How Deep Recurrent Reinforcement Learning and POMDPs Automate Orbital Collision Avoidance Under Sensor Uncertainty An autonomous satellite executing a real-time thruster burn in Low Earth Orbit to navigate space debris Author: Aarush Mahajan Primary Source: Spacecraft Autonomous Decision-Planning for Collision Avoidance: a Reinforcement Learning Approach (Bourriez, Loizeau, & Abdin, 2023) [1] Thesis Qu

Author(s): Anubhav Originally published on Towards AI. RTX 5090s rent for under $1 an hour while H100s cost nearly 50% more than in December. Which card fits which open model, RunPod vs Vast.ai vs Lambda, and when the API is still cheaper GPU rental sites list dozens of cards, from old gaming cards to NVIDIA’s newest datacenter ones, and the top of the list costs many times more an hour than the bottom. The prices make it look like you get what you pay for. After the price sticker shock of renti

Author(s): Udaykiran Estari Originally published on Towards AI. What Google’s open-source EGM plugin is, how its fresh-context reviewers check a design before any code exists, and why that differs from asking the same chat to check its own work. When I opened Google’s elephant-goldfish-model repository, the first line under the logo was a tagline: "Design with an elephant. Review with a goldfish." I had seen plenty of AI coding tools, but none that started by telling the AI to forget things on p

Last Updated on November 2, 2026 by Editorial Team Author(s): Latent AI Labs Originally published on Towards AI. My Model Thought Tamil Was Telugu: Open-Source Language ID for Hinglish, Tanglish and Kanglish By Arun Prabhakaran · @LatentAILabs The article introduces Bhasha, an open-source word-level language identification model for code-mixed Indian text (including Hinglish and other romanized/native-script mixes). It explains why message-level language ID is insufficient when you need per-word

Last Updated on November 2, 2026 by Editorial Team Author(s): Hayanan Originally published on Towards AI. Nine ordinary AI habits that quietly waste time, weaken judgment, and create technical problems, and how to replace them with workflows you can actually trust. The moth is still there, under a piece of aging tape. Someone found it caught in a relay of the Harvard Mark II computer, pulled it out, and fixed it to a page in the maintenance log. There was a physical cause. A physical place to lo

Last Updated on November 2, 2026 by Editorial Team Author(s): Montasir Mahmud Originally published on Towards AI. Mem0 made four decisions for you. Here is how to own every one of them with one SQL table, one Python class, and an update policy you can read. Suppose Priya, the user who has been following us through this whole memory phase, tells your agent: “Heads up, I started eating fish again.” Yesterday, Mem0 handled that sentence for you. A model read it next to her old “is vegetarian” memor

Last Updated on November 2, 2026 by Editorial Team Author(s): Paul Weng Originally published on Towards AI. I built an app to fill school forms for my kids. On forms it had never seen, the vision model alone found 62% of the blanks. Put a cheap detector in front of the same model and it found 97%. Here’s how I built it, and how I tested it. Note: I used AI tools for this article. I built the app with Claude Code, and an AI assistant ran the tests on my laptop and helped me shape the draft. The a

Last Updated on November 2, 2026 by Editorial Team Author(s): M. Haseeb Hassan Originally published on Towards AI. Tracing AI Agents: What OTel’s GenAI Spans Actually Capture On June 12, 2026, OpenTelemetry shipped semantic conventions release v1.42.0, and quietly pulled every gen_ai.* attribute, span, metric, and event out of its core repository [1]. If you had dashboards or alerting rules built against the old spec pages, they now redirect to a notice that reads "this page has moved and is no

Last Updated on November 2, 2026 by Editorial Team Author(s): Montasir Mahmud Originally published on Towards AI. A hands-on guide to Mem0’s add and search API, the model-driven ADD/UPDATE/DELETE step behind it, and wiring it into a LangGraph agent without losing track of what it changed Suppose Priya tells your agent in March that she is vegetarian, and in October that she has started eating fish again. Both sentences end up in your vector store as memories, and Day 35, the post on embeddings,

Last Updated on November 2, 2026 by Editorial Team Author(s): chakshu_salgotra Originally published on Towards AI. Part XIX — Spark on Kubernetes: Executor Sizing and Dynamic Allocation Kubernetes killed our executors every night for a week while the JVM insisted memory was fine. The JVM was telling the truth — it just wasn’t the memory Kubernetes was measuring. Part I | Part II | Part III | Part IV | Part V | Part VI | Part VII | Part VIII | Part IX | Part X | Part XI | Part XII | Part XIII | P

Author(s): Rittika Jindal Originally published on Towards AI. Choosing between a script, RAG, a fixed workflow, and an AI agent using four requests to the same support team. Imagine you’re automating a billing-support inbox. Four customers send four different requests: Does this ttask actually need an Agent?The article explains that whether you need an LLM, RAG, a fixed workflow, or an AI agent depends on separating message understanding from subsequent actions, and on what evidence is required

Last Updated on October 29, 2026 by Editorial Team Author(s): Sheqon Originally published on Towards AI. Reading, Writing, Sharing, and Interacting with the World through Video A Video Is a Trace of a World TL; DR. We treat a video as a partial, lossy, time-ordered observation of a world that keeps evolving whether or not the camera sees it. Four operations connect the two — reading (video → world), writing (world → video), sharing (one model does both), and interacting (the world advances under

Last Updated on October 29, 2026 by Editorial Team Author(s): Kasrasadatsharifi Originally published on Towards AI. Understanding the architectural decisions behind ConvNeXt, advantages and disadvantages over Vision Transformers and older CNNs. For years, CNN models dominated computer vision tasks such as classification, object detection, segmentaiton, etc. In 2020, with the introduction of Vision Transformers (ViTs) the focus shifted. They challenged CNNs dominance by introducing the global con

Last Updated on October 29, 2026 by Editorial Team Author(s): Hayanan Originally published on Towards AI. Calling everything AI is a little like giving every variable in a program the type any. It saves us the trouble of being specific. It also hides the mistakes we most need to catch. I would be worried about a codebase in which every variable was declared as any. Cover image — John McCarthy, one of the authors of the proposal that gave the field its name. Photograph: Tom Varco, Wikimedia Commo

Last Updated on October 29, 2026 by Editorial Team Author(s): Senthil Originally published on Towards AI. How the LLM (and the World Around It) Changed Forever — 2022 to 2026 Before we talk about tokens, let’s set the stage — because 2022 feels less like four years ago and more like a different geological era. The geopolitics: In February 2022, Russia launched its full-scale invasion of Ukraine — the largest land war in Europe since 1945. The world responded with sanctions, and energy prices wen

Last Updated on October 29, 2026 by Editorial Team Author(s): Hubert Nakielski Originally published on Towards AI. Five ways to wire memory into an AI agent, from vector RAG to temporal graphs to nightly “dreams”, drawn inside the whole harness, plus a 60-exchange demo that counts how long each one keeps the old answer. Every agent memory, whether a vector store, a graph or a folder of markdown files, can answer “what did the user tell me?”. They come apart when the user tells them something dif

Last Updated on October 29, 2026 by Editorial Team Author(s): David Pradeep Originally published on Towards AI. Building Crash-Safe Agent Workflows with Pi Durable and SQLite Moving autonomous coding agents out of interactive terminal loops and into unattended background workers exposes a brutal operational reality. When your Node process receives an unhandled SIGKILL, suffers an out-of-memory crash, or restarts during a multi-file refactoring sequence, standard stateless agent runtimes leave be

Last Updated on October 29, 2026 by Editorial Team Author(s): Montasir Mahmud Originally published on Towards AI. How vector search finds “I’m vegetarian” from “suggest dinner,” why it can’t tell a fact from its contradiction, and how to wire it into a LangGraph store Suppose a user named Priya asks your agent, “Suggest something for dinner tonight.” Somewhere in its long-term memory sits the single most relevant fact it has: “Priya is vegetarian.” The two sentences share no words. A keyword sea

Last Updated on October 29, 2026 by Editorial Team Author(s): Montasir Mahmud Originally published on Towards AI. Facts, past experiences, and self-written instructions each need their own storage, write trigger, and retrieval. Here are all three in one LangGraph agent. Picture the long-term store of an agent a week after launch. Under one user’s namespace sit diet: vegetarian, diet_update: eats fish now, and a one-off restaurant booking from last Thursday. They are all flat key-value pairs in o

Last Updated on October 29, 2026 by Editorial Team Author(s): Dhanya Shanbhag Originally published on Towards AI. Image generated with AI The accuracy of my regex and co-ordinate lookup based named entity recognition system broke suddenly from 94% to 67%. Only then did I realize that the purchase orders that were coming as input to my system were actually of different layouts. The header labels like the buyer name, delivery date, purchase order id etc. were all sitting at different places. The c