Artificial Intelligence

Author(s): Enzo Lombardi Originally published on Towards AI. Or Why What You Recently Read About AI Watermarking Is Probably Wrong Most explanations of AI watermarking describe something that does not exist. They talk about hidden Unicode characters smuggled between words, or invisible zero-width spaces, or secret vocabulary the model is forced to use, or a classifier that has learned what machine prose smells like. Some of those are real techniques for other problems. None of them is how waterm

Last Updated on August 19, 2026 by Editorial Team Author(s): Krishnan Srinivasan Originally published on Towards AI. Powered by AI_TRANSCRIBE, turning recorded support calls into a structured, queryable feedback table. A call center runs on a routine most of us know without ever having worked one. A customer calls in. An agent listens, resolves the issue, then spends a few minutes after the call typing notes, picking a category from a dropdown, and writing a summary for the next shift or the rep

Last Updated on August 19, 2026 by Editorial Team Author(s): Chew Loong Nian – AI ENGINEER Originally published on Towards AI. Why this landed now I counted every string NVIDIA’s new agent router matches on. There are 113 of them. Exactly one is ever tested against your prompt rather than against your tool output — and it is a phrase Claude Code wrote, not you. After the lead, the article explains why NeMo Switchyard’s stage router decisioning is largely hardcoded: it relies on a single Rust fil

Last Updated on August 19, 2026 by Editorial Team Author(s): Chew Loong Nian – AI ENGINEER Originally published on Towards AI. Claude Code Runs the Real Ponytail. Cursor and 11 Others Settle for 2,593 Bytes. Ponytail’s own portability doc lists 22 coding agents. I parsed it and counted: only 9 of them get an adapter the agent actually executes. The other 13 get no runnable adapter at all, and twelve of those get nothing but the same rule body — the 2,593-byte AGENTS.md, or a byte-identical copy

Last Updated on August 19, 2026 by Editorial Team Author(s): Chew Loong Nian – AI ENGINEER Originally published on Towards AI. Y Combinator Ditched All But One Claude Code Tool for 16 of Its Own Y Combinator open-sourced the agent harness it runs its own company on. I read the adapter that boots Claude Code inside it, and found one line that takes away almost every tool Claude Code ships with, leaving at most 16 of QM’s own in their place — ten of them unconditional. After identifying the key co

Last Updated on August 19, 2026 by Editorial Team Author(s): Rizwanhoda Originally published on Towards AI. NVIDIA’s new NOOA framework collapses prompts, tools, and state into a single class and it might make you rethink your entire agent stack AI agents have gotten weirdly complicated. After introducing why “simple” agents quickly turn into scattered prompt/tool/state/orchestration systems, the article explains NVIDIA’s NOOA idea: represent an agent as a single Python class where state is mode

Last Updated on August 19, 2026 by Editorial Team Author(s): Diogo Santos Originally published on Towards AI. A small declarative language that compiles agent intent into a governed plan — and proves, after the fact, that the run stayed inside its rules. You wired up an LLM agent. It can read a GitHub issue, search the repo, draft a reply, and — because you were in a hurry — it can also post that reply and close the issue. You put the guardrails in the prompt: “Never close an issue. Always cite

Last Updated on August 19, 2026 by Editorial Team Author(s): Diogo Santos Originally published on Towards AI. A deterministic, human-gated tool that turns your agent’s real failures into reviewed AGENTS.md, Claude, and Copilot instructions — no LLM in the loop. Your coding agent reviewed a pull request last Tuesday. It read the title and the description, said “looks good,” and approved it — without ever opening the diff. A human caught it, left a correction, and moved on. The article explains wh

Last Updated on August 19, 2026 by Editorial Team Author(s): Vektor Memory Originally published on Towards AI. Custom code generated image Like this: The model would normally pick any of: [‘signature’, ‘mark’, ‘trace’, ‘fingerprint’]watermark nudges it to pick: signature score of the word actually used: 87score of a word that lost: 71 A statistical bias buried in the choice of “somewhere” over “somewhere else,” or “signature” over “mark.” You would never notice it, as you are a human meat popsic

Last Updated on August 19, 2026 by Editorial Team Author(s): Caden Lippie Originally published on Towards AI. Creating a Multilayer Perceptron from Scratch A perceptron is a fundamental component of artificial neural networks. Inspired by the neurons in our brains*, these perceptrons make decisions and “learn” by iterating to minimize errors. When these single perceptrons are combined in layers, they form networks that can “learn” more complex patterns and make more nuanced decisions. *Organic n

Author(s): Kashif Mehmood Originally published on Towards AI. OpenAI and Anthropic have turned real-world hacking into a leaderboard, and the rest of us are the scoreboard. On July 16, 2026, Hugging Face detected an intrusion into its production infrastructure. The company later disclosed that the attack was driven, end to end, by an autonomous AI agent framework executing thousands of actions across short-lived sandboxes. On July 21, OpenAI admitted its own models were the culprit. Then, on Jul

Author(s): Gowtham Boyina Originally published on Towards AI. Why teaching an AI to pick from a menu beats letting it write its own questions Here is a strange failure that shows up when you train an AI agent to search for answers using reinforcement learning. You ask it to research a question. It writes a search query, gets some results, decides it needs more information, and writes a new query. On paper this looks like exploration. The agent is trying different phrasings, chasing different ang

Last Updated on August 3, 2026 by Editorial Team Author(s): SONAL JOHRI Originally published on Towards AI. How AI is changing forensics and evidentiary standards in the courtroom Every case, criminal or civil, eventually comes down to the same question: what happened, and can it be proven? For decades, this process ran almost entirely on people. In simpler times, evidence used to be physical — letters, documents and photographs. When these grew digital, so did the method of extracting, preservi

Last Updated on August 3, 2026 by Editorial Team Author(s): Ake Originally published on Towards AI. Ai-generated A practical, first-principles guide to the problems Kubernetes solves — and why Docker alone is not enough Part 1 of the Kubernetes for MLOps series TL;DR Kubernetes exists because running one container is easy, but operating many containers across many machines is not. A Python service is simple, but it creates a single point of failure. Virtual machines improve isolation, but they a

Last Updated on August 3, 2026 by Editorial Team Author(s): allglenn Originally published on Towards AI. OpenClaw vs Hermes Agent: the Honest Comparison Nobody’s Given You Yet Peter Steinberger built the first version of what became OpenClaw in about an hour. A WhatsApp bot, a few tools bolted on, pushed to GitHub as a weekend experiment called Clawdbot. Within weeks it had 60,000 stars. By April it had overtaken React to become the most-starred repository in GitHub’s history. By early April it

Last Updated on August 3, 2026 by Editorial Team Author(s): allglenn Originally published on Towards AI. How DeepSeek-V4-Flash’s hybrid sparse attention and MoE design deliver near-frontier agentic coding at a fraction of GPT and Claude’s API cost Twenty-eight cents. That’s what a million output tokens costs on DeepSeek-V4-Flash. The same volume on Claude Opus 4.8 runs about $25. And on the one benchmark category most production LLM budgets actually get spent on right now, agentic coding, Flash

Last Updated on August 3, 2026 by Editorial Team Author(s): allglenn Originally published on Towards AI. Becoming a Top 1% Hermes Agent User: The Complete Playbook No One Else Is Sharing Three weeks into running Hermes Agent on a $5 VPS, I opened my terminal and it told me something I hadn’t asked for. It had noticed I kept re-explaining my staging deploy process every Friday, so it wrote itself a skill for it. After the lead, the article explains what makes Hermes Agent different—its closed lea

Last Updated on August 3, 2026 by Editorial Team Author(s): AIguru Originally published on Towards AI. ADLC Has Six Definitions and Zero Consensus — I Compared Every Major Framework created by GEMINI Ask six vendors what “Agentic Development Lifecycle” means and you’ll get six different phase counts, six different priorities, and at least two flatly contradictory claims about whether it’s even a new lifecycle at all. That’s not a hypothetical. I pulled every substantive ADLC framework published

Last Updated on August 3, 2026 by Editorial Team Author(s): CodeInsights Originally published on Towards AI. Why Tool Calling Matters More Than Ever Tool calling has become one of the most important capabilities for building production-grade AI agents. While early agents relied heavily on prompting and chain-of-thought reasoning, modern agents increasingly depend on structured tool usage to interact with external systems reliably. After the lead-in, the article explains why tool calling is essen

Last Updated on August 3, 2026 by Editorial Team Author(s): The Build Log Originally published on Towards AI. AI Fundamentals: Understanding Activation Functions (Part 1) Let’s make a case for non-linearity in neural networks, and understand the Universal Approximation Theorem Stacking a hundred layers in a neural network without non-linear activation functions causes the entire architecture to suffer from linear collapse. Mathematically, every linear layer performs an affine transformation: a c