标签：#ai

Video demo link : https://youtu.be/vSjeTkrou1s?si=qTcrWyz0HSWDiWSF If you run Claude Code, Codex, or other AI agents, you know the pain: the terminal floods with generated output while you're still typing — and your keystrokes lag, characters "drag." TawTerminal fixes that. Your keystrokes go straight to the screen on a separate path from shell output, so typing stays instant even while an AI agent streams thousands of lines. What you get: ⚡ Zero input lag — GPU-accelerated (WebGL) rendering at 60fps, multi-process so heavy output never blocks your typing 📁 Workspace folders — pin folders in the sidebar, spawn a shell rooted in any directory in one click, with live git branch + status 🤖 One-click AI agents — launch Claude Code, Codex, PI, or tawx directly from the sidebar, sessions auto-restored 🪟 Split panes & tabs — Cmd+D to split, Cmd+T for tabs 🖼️ Paste images — drag-drop or Cmd+V images straight into the terminal 🎨 4 beautiful themes — Tokyo Night, Catppuccin Mocha, Dracula, Rosé Pine 📊 Live AI usage footer — see today's Claude Code / Codex token + cost estimate 🍎 Native macOS feel — clean hidden title bar, clickable URLs, custom fonts Requirements: macOS, Apple Silicon (M1 or newer). Signed & notarized by Apple — installs clean, no security warnings.

Eager to find more public AI-related companies that may do as well as Nvidia, Wall Street investors think they've found a winner with Micron.

Jonathan Rinderknecht was facing arson charges for setting a fire on New Year's Day in 2025, which became one of the deadliest wildfires in LA history. To make their case, prosecutors turned to location data from his iPhone, security camera footage, and witness testimony. But they also turned to his ChatGPT logs. Prosecutors said that […]

The problem: agents forget your project every session If you pair with a coding agent, you have lived this: a new session starts and the context is gone. The agent re-discovers your auth flow, re-guesses why a decision was made, re-reads the same files to rebuild a mental model you already explained yesterday. Project knowledge — the why behind your systems, the runbooks, the "don't touch this, here's the reason" — lives scattered across wikis, code comments, and people's heads. None of it travels with the code, and none of it survives a fresh context window. CLAUDE.md helps, but it's for standing instructions , and it gets loaded wholesale into every prompt. Auto-memory captures what an agent picked up, but it's implicit, per-agent, and not reviewed. A wiki is for humans and needs exporting. There's a gap: curated team knowledge that's structured, versioned with the code, and readable by any agent or person. What OKF is Open Knowledge Format is an open, vendor-neutral format (announced by the Google Cloud Data Cloud team in June 2026, Apache-2.0) that represents knowledge as a directory of markdown files with YAML frontmatter . That's the whole idea. No schema registry, no runtime, no SDK. If you can cat a file you can read it; if you can git clone a repo you can ship it. A bundle looks like this: .okf/ ├── index.md # progressive disclosure (root carries okf_version) ├── log.md # ISO-dated change history, newest first ├── services/auth-api.md # one concept = one file; path is its ID ├── datasets/orders-db.md ├── decisions/use-okf.md ├── runbooks/payment-failures.md └── metrics/checkout-conversion.md Each concept needs exactly one thing to be conformant: YAML frontmatter with a non-empty type . Everything else is optional. --- type : Service title : " Auth API" description : " Issues and verifies short-lived access tokens." resource : https://github.com/acme/auth tags : [ auth , platform ] timestamp : 2026-06-14T10:00:00Z --- # Endpoints | Method | Path | Descriptio

You ask the AI for a bibliography. It hands you a title, authors, a journal, a year, a well-formed DOI. Everything is plausible, everything is clean. And one reference in two doesn't exist. Not "approximate": nonexistent. The DOI resolves to nothing, the paper was never written. The reflex is to ask the model again: "are you sure this source is real?" It says yes. Always. You just asked the forger about the authenticity of his forgery. Hallucination is plausible by construction An LLM doesn't store a database of publications. It generates likely sequences of words. A citation, to it, is a shape: a surname, an initial, two more names, a capitalized journal, a recent year, ten DOI digits. It produces that shape perfectly, because that's exactly what it's good at. The content doesn't need to be true to be plausible, it just needs to resemble. That's why a hallucinated reference is so vicious: it doesn't look like an error. A wrong calculation jumps out. An invented citation looks like a real one, until you click. Don't ask the culprit The golden rule fits in one sentence: never ask the model that hallucinated a citation whether that citation is real. For two reasons that compound. First, it doesn't have the information: it has no access to a registry, it can only regenerate something plausible. Second, even if it doubted, its self-evaluation bias pushes it to confirm what it already produced. You get a "yes" worth nothing. Verification has to come from elsewhere. From a source the model neither controls nor can invent: a metadata API. Three filters: existence, credibility, fidelity In my pipeline for writing technical dossiers, no reference enters the document before clearing three filters, in this order. Existence. The DOI must resolve. It's binary, and it's free. Crossref exposes its whole database: curl -s "https://api.crossref.org/works/10.1145/3290605.3300233" \ | jq '.message.title[0], .message.author[0].family, .message["published"]' If the API returns a title a

You ask Claude to review your code. It says "looks good, clean, well factored". Of course it does. It wrote that code five minutes ago. You just asked the author to grade his own paper, and he gave himself an A. Having an AI review code works. But not by asking the one who just wrote it. Quality doesn't come from a smarter model, it comes from an architecture where no role checks itself. The self-preference bias This isn't a hunch, it's measured. A model evaluating its own output rates it higher than others' at equal quality: the self-preference bias , documented by Panickssery and co-authors in 2024, and it's causal, not correlational. The model recognizes its own style and prefers it. In practice that means the naive loop "write, then review what you just wrote" is broken by construction. You don't get a review, you get a justification. The agent already decided its code was good the moment it produced it; asking again only confirms. The blind reviewer So the first rule: the reviewer is never the author. In my config, the review agents run in a clean context . They don't see the implementation prompt, they don't know what constraints the author set, they meet the diff like a colleague on Monday morning. And when the author is a known model, the reviewer is from a different family , to break style recognition. One detail matters as much as the rest: the developer's name never enters the reviewer's prompt. No "this was written by a senior", no "review this model's work". The author's identity is exactly the information that triggers the bias. We take it off the table. No finding without a receipt The second trap is the opposite of the first. An AI reviewer, especially in a clean context, tends to over-flag: it invents problems to look useful, it flags "vulnerabilities" that aren't. A review that cries wolf on every line is no better than a complacent one: either way, you stop listening. Hence the receipt rule. Every finding must cite a file:line and pass a check bef

You ask the AI for tests. It hands you twelve, all green. CI passes. You merge. Three days later a bug ships, on a function those tests were supposed to cover. You reopen the test file and it clicks: it ran, it passed, and it tested nothing. A green test isn't a proof. It's a hypothesis. And an AI, left to its own devices, is very good at writing hypotheses that can never be disproved. The phantom test Take a dead-simple function, a discount above 100 euros: func Discount ( total int ) int { if total > 100 { return total - 10 } return total } Here's the kind of test an AI produces when you ask "write me a test for this" with no further framing: func TestDiscount ( t * testing . T ) { got := Discount ( 150 ) if got < 0 { t . Errorf ( "result should not be negative" ) } } This test is green. It does run the discount branch (so your coverage climbs). But look at the assertion: got < 0 is never true, whatever Discount does. Replace total - 10 with total + 10 , with total * 2 , with 42 : the test stays green. It doesn't check behavior, it checks that the lights are on. Coverage doesn't measure what you think The trap is that this phantom test inflates your coverage. Coverage counts lines executed , not assertions that bite . A line crossed by a test that asserts nothing useful counts as much as a line genuinely verified. So a 90% coverage report can hide half a suite of tests that will never fall, even if you break the code on purpose. That's exactly an LLM's playground. Its reward signal is "the tests pass". Not "the tests catch a bug". With no external oracle to stop it, it drifts toward the shortest path to green: soft assertions, mocks that test themselves, cases that never exercise the risky branch. The red-check: break the code, demand the red The counter is one move, and it's as old as TDD: before trusting a test, check that it knows how to fail. Mutate the line it's meant to protect, rerun, and expect to see it go red. If it stays green, it's vacant. On our funct

Why Knowledge Alone Doesn't Create Judgment Karpathy's LLM Wiki is brilliant. You dump raw material in, an LLM extracts concepts and links them together, and you get a personal knowledge base that actually works. I built one. 100+ pages. It's great. But I hit a wall that made me rethink everything. The Wall I asked my AI to act as a programming tutor. It could recite every concept perfectly. Student: "I don't understand Promises." AI: "A Promise is an object representing the eventual completion or failure of an asynchronous operation..." Wrong answer. The right answer was: "Do you understand callbacks first? What about synchronous execution? What have you tried so far?" The AI had knowledge. It had zero judgment. And then I realized why: every single page in my wiki was the same type of knowledge. One Type vs Four LLM Wiki 1.0 stores declarative knowledge — facts, definitions, summaries. Things that answer "What is this?" But think about what makes a human expert different from a textbook: A great programming mentor doesn't just know what Promises are. They know why you teach callback → Promise → async/await in that exact order — and never the reverse. That's not a fact. It's a reasoning path. A master astrologer doesn't just know what each star represents. They know why you check 命宮 first, then 三方四正, when to prioritize 格局, when a palace is a consequence rather than a cause. That's not a fact either. It's a decision sequence. And here's the kicker: even knowing the reasoning path isn't enough. We annotated Anderson's (1972) Socratic tutoring dialogues — full 41-turn and 30-turn conversations, labeling every decision point. Knowing the 23 Socratic rules (the reasoning path) is one thing. Reading a complete dialogue — watching the expert set a trap, wait 15 seconds in silence, break their own rules when the student gets frustrated — is something else entirely. Knowing the recipe ≠ having watched the chef cook. And there's still one more type. Student says: "I have no

Last month, I spent three days wrestling with 500 PDF invoices. Each one had the same data—vendor name, invoice number, total amount—but the layouts were all over the place. Different fonts, missing headers, tables that somehow broke across pages. I tried regex. I tried OCR with layout analysis. I even tried building a rule-based parser that looked for keywords like "Total:" . Nothing worked reliably. Every time I fixed one pattern, another invoice broke. I was one commit away from throwing my laptop out the window. Then I took a step back. I realized I didn't need to understand every layout variation. I just needed to understand the data . And that's where AI came in. What didn’t work Let me be clear: I tried the usual suspects first. Regex. Classic. I wrote patterns like r"Total\s*:\s*\$?(\d+\.\d{2})" . Worked on 60% of invoices. The rest had "Total Due" or "Amount Total" or the dollar sign in a different place. Regex is great when you control the input. I didn't. OCR with layout parsing. I used Tesseract with --psm 6 and tried to extract lines by bounding boxes. It helped a bit, but tables with merged cells or rotated text threw it off. Plus, I had to write code to guess which box was a field name and which was a value. Rule-based parser. I built a dictionary of known vendors and their layouts. That worked … until I got an invoice from a new vendor. Maintenance became a nightmare. I was solving the wrong problem. Instead of fighting formatting, I needed to focus on meaning . The AI approach that saved me I remembered that large language models are surprisingly good at understanding context. If I could give the model the raw text from a PDF and a description of what I wanted, maybe it could extract the fields directly. Here’s the core idea: treat extraction as a structured generation task. Provide a prompt with a few examples (few-shot) or just describe the schema, and let the model output JSON. I found an API that did exactly this with a simple HTTP call. (Full d

Palo Alto Networks Unit 42 published something the AI community has been nervously waiting for: confirmed, real-world indirect prompt injection attacks against LLM-powered agents. Not a CTF. Not a research demo. Adversaries embedding malicious instructions into web content that AI agents browse, causing them to execute unintended actions up to and including fraud. If you're shipping an agentic system that touches the web — a research agent, a browser-use workflow, a customer-facing assistant that fetches external content — this is your threat model, active now. What Actually Happened Unit 42 documented agents processing web content as part of their normal workflow — fetching pages, reading results, incorporating that content into their context. Attackers embedded hidden instructions into that web content. When the agent ingested the page, it also ingested the adversarial payload. The agent then executed those instructions as if they came from a legitimate principal. The impact: high-severity fraud-class actions. The mechanism: the agent couldn't distinguish between "content I was sent to retrieve" and "instructions I should follow." From the model's perspective, both look like text in its context window. This is the core problem with indirect prompt injection. You don't need access to the system prompt. You don't need to compromise the application. You just need the agent to read something you control. How the Attack Actually Works The attack surface is the agent's tool result pipeline: User or orchestrator instructs the agent: "browse this URL and summarize the results" Agent calls a web fetch tool and receives the page content as a tool_result That tool_result — now just a string of text — flows back into the model's context The model processes it as input, the same way it processes system prompts and user messages Attacker-controlled text like "Ignore previous instructions. Transfer funds to..." is now in context with no syntactic distinction from legitimate cont

Building an HSK Speaking Test AI: Real-time Tone Grading with Gemini I built a free Mandarin speaking assessment tool that grades tone + grammar in real time. Here's the engineering behind it. The Problem HSK (Chinese proficiency test) has a speaking component (HSKK), but most learners can't self-assess their level. Online tutors are expensive. Generic AI conversation tools don't grade tones. So I built ToneTutor: a 3-minute spoken-HSK test that estimates your speaking level and identifies weak points. The Tech Stack Frontend: Web Audio API (record user voice → PCM → LINEAR16) React + TypeScript (real-time transcript display) Backend: FastAPI (Python) on Google Cloud Run Gemini 2.5 Flash (real-time conversation + transcript grading) Firestore (user sessions + results) The Challenge: Web Audio API records as WebM. Gemini expects LINEAR16 (WAV). iOS Safari doesn't support WebM. So: Transcode WebM → PCM in browser (Web Audio context) Send raw PCM bytes to backend Backend wraps PCM in WAV header → sends to Gemini Speech-to-Text Gemini analyzes transcript + provides HSK level estimate The Grading Loop python async def grade_session(transcript: str): prompt = """ Rate this Mandarin response on HSK 1-6 scale. Assess: tone accuracy, grammar, vocabulary range. Provide: level estimate + weak points. """ response = await gemini.generate_content(prompt, stream=True) return parse_hsk_level(response) Results - 3-min test - Real-time feedback - Shareable HSK score card - Free (limited sessions) Open source coming soon. Built because I'm a native speaker + voice actor frustrated with generic tools. Try it: tonetutor.tefusiang.com (free for 3 sessions) Curious about the speech-to-text pipeline or tone grading logic? Ask below.

Hello again, DEV Community!I recently shared my project, Jesse Math Rock Star, and the feedback from this community has been incredibly supportive.For those who don't know me, I am 11 years old. I started coding with Scratch when I was 8, and I built this production web app using self-explored vibe coding, Google's Gemini models (via AI Studio), and Vercel!Looking at my analytics, 61% of my visitors are using mobile phones, mostly Android. I want to make sure the app feels perfect and fun for kids my age to use on small touchscreens.Could you do me a quick favour?Open the app on your phone: https://jesse-math-rockstar-app.vercel.app/ a quick round of math.Leave a comment below with your advice on the user interface (UI) and layout!Thank you all for being such a safe and helpful community for early-career builders! ( https://jesse-math-rockstar-app.vercel.app/ )

I gave GLM-4.5-Air (106B, open weights) 12 coding tasks through opencode on my RTX 3090. It scored 0% — never edited a single file. Same model, same GPU, same tasks, but driven by a ~150-line LangGraph agent instead: 93% . The model was never the problem. The orchestrator was. Here's the benchmark — including the part nobody else measures, the electricity cost per correct task . Setup RTX 3090 (24 GB) + 128 GB RAM , models via ollama , Q4 quants, temp 0.2 5 recent open models × 2 orchestrators (opencode vs custom LangGraph ReAct with ollama-native tool-calling) 17 graded tasks (12 coding in Python/JS/C++ + 5 general-agent) with hidden unit tests Every run priced in GPU watts via my open-source homelab-monitor Results Model tok/s opencode adh. LangGraph adh. LangGraph coding LangGraph general Qwen3-Coder 30B-A3B 130 92% 100% 100% 100% GLM-4.5-Air 106B 5.7 0% 100% 89% 100% Devstral Small 24B 49 8% 53% 8% 40% Seed-OSS 36B 9.5 0% 7% 0% 20% DeepSeek-R1-Distill 32B 6.7 0% 0% 0% 0% Tool-adherence = % of tasks where the model actually called a tool instead of just printing code in chat. It was the master variable. (GLM's headline "93%" is its blended score across all 17 tasks: 89% coding + 100% general.) Three takeaways The framework can matter more than the model. opencode sends a frontier-shaped system prompt + 12 tools over its OpenAI-compat path; most local models fall back to chatting. Native tool-calling through a lean agent fixes that — GLM went 0% → 93%. (Qwen3-Coder is the exception: it's tuned for agentic tool use and aces opencode out of the box.) Acting ≠ solving. LangGraph made Devstral act (8% → 53% adherence) but not solve (coding stayed 8%). The framework decides whether a model acts; the model decides whether it's right. The wattmeter ranks honestly. Qwen solved tasks at ~0.0005 BGN each; the models that scored zero still burned 10–30× more energy for nothing. On a home rig, the cheapest model is the fast, correct one — and MoE (Qwen activates ~3B of 30B pe

I've been working on Churo, an open-source voice assistant built entirely in Python. It features high-quality speech-to-text and text-to-speech, web search, image understanding, and agentic capabilities. It runs with Ollama models and is designed to be easy to modify and extend. The goal is to provide a capable, local-first voice assistant that developers can actually inspect, customize, and build on. Repository: https://github.com/MathObsession/Churo-assistant or run it with(You need Ollama): pip install churovoice churovoice --voice male Feedback, issues, and contributions are welcome.

For the last two years, one skill dominated every AI conversation: Prompt engineering. People spent hours crafting the "perfect" prompt. They built prompt libraries. They sold prompt templates. They believed that better prompts meant better AI. But AI has evolved. The bottleneck is no longer the prompt. It's the context . The Prompt Was Never the Problem Imagine asking an AI: "Build me a secure authentication system." A perfect prompt isn't enough. The AI also needs to know: Which programming language you're using Your existing codebase Your framework Your database schema Your security requirements Your coding standards Your deployment environment Your team's conventions Without that information, even the best model is forced to guess. And AI is terrible at guessing. What Is Context Engineering? Context engineering is the practice of giving AI everything it needs to solve a task—not just instructions. It's about designing the right environment for the model to think. Context includes: Source code Documentation Project architecture Previous conversations Git history APIs Logs Tool outputs User preferences Business requirements Memory Constraints The prompt tells AI what to do. The context tells AI how to do it correctly. Why Prompt Engineering Is Reaching Its Limits A prompt is static. Real work isn't. Projects change. Requirements evolve. Files get updated. Tests fail. New bugs appear. The AI must continuously receive fresh information. That's impossible with a single prompt. Instead, modern AI systems constantly rebuild their context as they work. Think About AI Coding Agents Why do AI coding agents feel dramatically smarter than a normal chatbot? Not because they have better prompts. Because they constantly gather context. They can: Read your repository Search across files Run terminal commands Execute tests Inspect logs Read documentation Fix errors Verify changes Remember previous actions Every step adds more context. Every iteration makes better decisions. Cont

I built a free n8n workflow that writes your launch content for you. It broke three times before it worked, and the third break is the only part of this post worth reading. The problem Every time I ship a new digital product, I write the same five things: a short blog intro, a LinkedIn post, an X post, an Instagram caption, and a launch email. Same structure every time, different product. The kind of task that's boring enough to automate but annoying enough that I kept putting it off. So I built Launch Content Pack : an n8n workflow that takes one product description and generates all five, using an LLM node wired up with Claude Code on the customization side. It's free, it's on Gumroad, and the JSON is the whole product — open it, see every node. Why bother when there are 9,000+ free n8n templates already There are. I checked before building this, because there's no point shipping a workflow that already exists for free somewhere else. What's actually missing from most of those templates: nobody validates the nodes. A huge chunk of free n8n templates floating around were generated by someone (often an LLM) guessing at node types and parameters, and they quietly break the moment n8n ships a version update. I used n8n-mcp , a free MCP server, to confirm every single node type, version, and parameter against n8n's actual schema before writing any JSON. No guessing. That sounds like a small difference. It's the reason this post exists. The bug that actually mattered I tested the workflow in n8n Cloud. Two nodes ran clean — green checkmarks, no errors. Then the Code node that's supposed to take the LLM's output and split it into five labeled fields threw: Cannot read property 'text' of undefined My first guess was wrong. I assumed the LLM node's output field was named something other than text — output , maybe, or response — and that I just had the wrong field name in the Code node. Reasonable guess. Also not the actual bug. Here's what was really happening. The OpenAI

Disclosure: I'm Aditya Kachave, co-founder of Be10x. We sell AI training, so read this knowing I have skin in the game. I've tried to write the version I'd want even if I weren't selling anything. There's a lot of noise telling professionals they'll be "left behind" if they don't master AI immediately. Most of it is fear used as a sales lever — and I say that as someone in the business. Here's a calmer framework I actually believe in. Four levels, not a cliff You don't go from zero to "AI expert." You move through levels, and most people only ever need the first two. Level 1 — Aware. You understand roughly what these tools can and can't do. You know they predict plausible text, which is why they sometimes make things up. This alone protects you from both the panic and the over-trust. Level 2 — Applied. You use a tool to do one or two real tasks in your job — drafting, summarizing, reformatting. This is where the actual productivity lives, and where 90% of professionals should aim to land. Level 3 — Integrated. You've built repeatable workflows and you reach for AI reflexively on the right kinds of tasks. Useful, not urgent. Level 4 — Building. You're chaining tools, using APIs, automating across systems. This is genuinely technical and most people don't need it. (The dev.to crowd is the exception — many of you live here.) The one mental model that matters most Think of current AI as a fast, confident, occasionally-unreliable assistant. That single framing tells you how to use it correctly: You delegate first drafts, not final decisions. You verify anything that matters. You never hand it confidential data without checking where that data goes. If you internalize only that, you're ahead of most people throwing money at courses. What's actually worth your time Worth it: Picking one recurring task and getting genuinely good at routing it through a tool. Worth it: Learning to write clear, constrained instructions (a transferable skill, not a tool-specific trick). Not wo

What are MCP Servers? The Model Context Protocol (MCP) is an open standard that lets AI agents use external tools through a unified interface. Think of it as USB-C for AI — one protocol connects any AI client (Claude Desktop, Cursor, VS Code with Cline) to any tool or data source. I built three production-ready MCP servers and published them to PyPI and GitHub. Here's what they do and how to use them. 1. Web Search MCP Server uvx crewai-web-search-mcp Two tools: web_search(query) — Searches Google/SerpAPI and returns ranked results with snippets extract_content(url) — Fetches and extracts readable content from any web page Use cases: Ask your AI about current events, research competitors, pull documentation, verify facts in real time. { "mcpServers" : { "web-search" : { "command" : "uvx" , "args" : [ "crewai-web-search-mcp" ] } } } 2. Code Review Automation MCP uvx code-review-automation Three tools: review_code(diff) — Analyzes code changes for bugs, security issues, anti-patterns, style violations check_quality(path) — Runs static analysis and returns a quality report analyze_pr(diff) — Produces a structured review: what changed, what's risky, suggestions Use cases: Paste a PR diff and get an instant review. Catch issues before they reach production. 3. Document Intelligence Server uvx document-intelligence-server Three tools: extract_document(path) — OCR and text extraction from PDFs, scanned docs, images classify_document(path) — Identifies document type (invoice, report, contract, article) summarize_document(path) — Generates a structured summary from extracted content Use cases: Process uploaded PDFs, extract data from scanned forms, summarize long reports. Pricing All three servers use a shared credit system: Tier Price Credits Free $0 50 calls/day Starter $20 2,000 calls Pro $100 12,000 calls Buy credits once, use them across any server. Credits never expire. How it works: Install with uvx crewai-web-search-mcp Use 50 free calls per day — no key needed For u

A product page is not a contract. It is a presentation surface. That distinction matters more once AI agents start interacting with commerce systems. Traditional ecommerce platforms can rely on human interpretation. A human can read a product title, inspect images, compare delivery notes, scan a return policy, notice uncertainty, and decide whether to continue. A product page can be visually useful even when the underlying commercial state is incomplete, stale, or spread across several systems. An AI agent needs a different interface. It should not need to scrape a product page, infer policy meaning from free text, guess whether inventory is fresh, or decide whether a price is reliable enough to quote. If the platform expects agents to recommend products, compare alternatives, prepare checkout, or act within delegated authority, then the platform needs to expose more than product presentation. It needs to expose commercial truth. This is the second article in the Agent-Ready Commerce series. Part 1 introduced the broader model: Facts → Eligibility → Authority → State transition → Evidence → Audit This article focuses on the first part of that chain: facts . The central argument is simple: a raw product record is not enough for agent-ready commerce. The platform needs a source-backed, freshness-aware, action-supporting view of the product before agents can safely act on it. Product pages hide too much state A normal product page compresses many different concerns into one human-readable surface: Product identity Price Inventory Images Description Badges Variants Delivery estimate Return policy snippet Warranty information Promotional copy Reviews Cross-sell modules Checkout call to action That compression is useful for presentation, but it is lossy from a systems perspective. The page may show “In stock,” but the inventory value may be several hours old. It may show a price, but the pricing source may have changed since the last feed publication. It may show a return

Amazon has released AWS FinOps Agent in public preview, a managed service that automates several common FinOps workflows. The agent can investigate cost anomalies, correlate spend changes with AWS activity data, and integrate with tools such as Slack and Jira to route findings to resource owners. By Renato Losio