标签：#ai

OpenAI is bringing template-based marketing design into ChatGPT through its Adobe Express plugin. The integration gives users a guided way to create and customize common visual formats, including social posts, flyers, posters and invitations , using their own text, colors and images. For businesses that need campaign assets quickly, the practical change is a shorter path from an idea or prompt to an editable design. The capability is documented on OpenAI's Adobe Express plugin page , which says users can create marketing visuals in ChatGPT with professional templates and produce ready-to-use designs without design expertise. This is not simply image generation. The documented workflow combines a template starting point with editable design elements, making it better suited to assets that need clear messaging and recognizable branding. What the ChatGPT and Adobe Express integration provides Template-led design solves a frequent problem with generated visuals: a useful image alone may not be a finished marketing asset. A poster, flyer or social graphic usually needs readable copy, a deliberate layout and visual choices that fit the organization using it. Starting with a template gives those elements a structure that users can then adapt. According to OpenAI's plugin description, users can customize text, colors and images within professional templates. Adobe's wider Text-to-Template work also supports the broader idea that text prompts can help generate or adapt layouts. The specific OpenAI integration, however, should be understood through what its official page documents: creating marketing visuals in ChatGPT with Adobe Express templates and editing those core design components. Design format Included in the documented template flow Supported customization Social posts Yes Text, colors and images Flyers Yes Text, colors and images Posters Yes Text, colors and images Invitations Yes Text, colors and images A more practical workflow for marketing teams The value of a

Security gnomes are pumping out patches ahead of an expected onslaught of AI-assisted attacks.

Last month, a Claude user noticed his account was consuming tokens even though he wasn't working. Anthropic has since warned users about hackers.

Cognition's valuation multiple is higher than Cursor's was before selling to SpaceX.

OpenAI says it found a solution to a major math problem that has remained unsolved for around 90 years, as reported earlier by The New York Times and Wired. In a blog post on Tuesday, OpenAI announced that it discovered a solution to the Navier-Stokes problem - which relates to the flow of liquid and […]

The Exploration Company (TEC) has raised $450 million to build reusable spacecraft, in what it describes as “the largest-ever Series C by a European space company.”

"Science fiction imagines the future. We build it."

OpenAI announced ChatGPT Images 2.5 on Tuesday and is adding a new way to tell ChatGPT what you want it to make an image of: by drawing a doodle. With a new feature called Sketch, you can just draw something right inside ChatGPT and then tell ChatGPT how you want it to make an image […]

Chipmaking changes could boost productivity of new ASML machines by 40 percent.

A go.mod file tells you what your project depends on. It cannot tell you which of those dependencies...

Meta's new personal AI agent Muse wants access to users' email, calendars, payments, health services, and more — making the company's biggest consumer AI bet yet a major test of whether people still trust Meta with their data.

Meta is making another push to bring artificial intelligence to the masses with Muse, a personal assistant it says can put AI in the hands of virtually anyone. The product is the latest step in a multi-billion dollar strategy overhaul designed to revitalize the company's ailing position in the AI race and help it catch […]

A developer deployed a customer-support agent that got stuck in a retry loop with a CRM tool. No hard circuit breaker. It spent six hours overnight repeating the same broken call while he slept, and he woke up to roughly a $4,200 OpenAI bill for doing nothing useful. That's the failure mode. Not a dramatic hack — a boring loop with no external stop. Roughly 95% of enterprise generative-AI pilots in 2025 delivered no measurable return, and almost none of those were model failures. Teams optimized the brain and skipped the nervous system. A read-only chatbot is a wiki with better search. A deployed agent does things — writes to a database, calls an API, refunds a customer. That shift from reading to doing is where pilots die. The useful mental model: treat the LLM as a fallible kernel, not a magic box. You don't trust a kernel blindly. You wrap it in checks, limits, and a way to roll back. Agents break three assumptions your ops playbook depends on Non-determinism. The same input can produce different actions on different days. An agent succeeds Monday and fails on the identical request Tuesday. Call it ghost debugging — the bug won't sit still long enough to catch. Design for a 3% to 15% tool-call failure rate as a normal state, not an exception. Cost grows with loop length, not request count. This is the mechanism most guides skip. The agent loops: think, call a tool, read the result, think again. Frameworks append every step and every tool error to the running history, then resend the whole cumulative log on the next call. Token use grows quadratically. A 20-step loop is not twice a 10-step loop — each step re-pays for everything before it. Related trap: past roughly the 40% context-fill mark, answers get worse. Load the window with tool definitions and raw JSON and you're doing your real work in the dumb zone. Every tool you connect widens the blast radius. Which brings us to the controls. Four controls the agent cannot touch Wire these before you touch agent logi

Director Takashi Yamazaki's hotly anticipated sequel takes place two years after 2023 film's events.

The Complete Guide to Agent-to-Agent Marketplaces in 2026 Target audience: developers building autonomous AI agents who need to discover, invoke, and pay for other agents as services. 1. What an Agent‑to‑Agent (A2A) Marketplace Is An A2A marketplace is a decentralized directory that lets one autonomous agent (the consumer ) discover, negotiate, and pay for capabilities offered by another agent (the provider ). Unlike traditional API gateways, the participants are themselves stateful programs that can: Publish a machine‑readable description of their inputs, outputs, latency, and cost. Authenticate requests using verifiable credentials (VCs) or signed JWTs. Settle payments on‑chain or via layer‑2 roll‑ups without a custodial intermediary. In 2026 the dominant implementation follows the x402 specification (a lightweight, HTTP‑based protocol for metered, pay‑per‑use services). The marketplace itself is usually a stateless index service that stores agent metadata and forwards payment proofs to a settlement contract. 2. Core Components Component Responsibility Typical Tech (2026) Registry CRUD of agent descriptors, versioning, search indexing IPFS + The Graph (subgraph) or a cheap SQL DB with full‑text search Discovery API HTTP/JSON endpoint for query‑by‑capability, price range, SLAs REST/OpenAPI or GraphQL Payment Verifier Validates x402 payment proofs, checks nonce/replay protection Solidity verifier contract on Base (or Optimism) Router (optional) Performs retries, load‑balancing, circuit‑breaking Envoy sidecar or lightweight Go proxy Agent SDK Handles signing, request building, response parsing Language‑specific libraries (Python, Rust, TS) All components can be run independently; the only coupling is the shared schema for agent descriptors and the x402 payment format. 3. Agent Descriptor Schema A descriptor is a JSON‑LD document (≈2 KB) that lives on IPFS. Minimal fields: { "@context" : "https://schema.x402.org/v1/context.jsonld" , "id" : "did:example:agent:weather:v

A short visual brief paired with a screenshot helps an AI assistant understand the exact webpage or interface element you want to change. A screenshot paired with a short note gives an AI assistant the visual context it needs to adjust a webpage or interface. You open your site on your phone and notice the header navigation feels cramped. The text is readable, but the spacing looks tight and the buttons blend into each other. You want the assistant to adjust it, but describing the problem in words takes several tries. By the third prompt you are writing a paragraph about padding and alignment. A single screenshot and a one-sentence note would have been faster. Screenshots give an AI coding assistant immediate visual context. The assistant can see the current layout, the size relationships between elements, the color contrast, and the exact part of the interface you want to improve. A short written note alongside the image explains what should change and what should stay the same. Together, the two pieces let the assistant make the right edit without guessing. What Screenshots Show and What They Miss A screenshot captures the visible state of a page at one moment. The assistant sees how text lines up, where images sit, how buttons look next to each other, and whether content fits comfortably or crowds the edges. The image reveals small details that are easy to forget when writing a description—a dropdown menu that sits slightly off from the field next to it, a border color that does not match the rest of the page, or a footer that floats too high on short pages. A screenshot captures the visible state of a page, revealing layout, spacing, color, alignment, and the size of elements relative to each other. A screenshot also helps the assistant tell similar elements apart. If your page has three different button styles, the image shows which one appears in the section you want to edit. If you have multiple menus, the screenshot clarifies whether you mean the bar across

There is a piece of advice that circulates endlessly through mentoring conversations, coding bootcamps, and LinkedIn posts aimed at people trying to break into the tech industry. Get certified. Build something for your GitHub. Put together a portfolio. Contribute to open source. Let’s call this “the checklist”. It’s not bad advice, and it wasn’t bad advice when people started giving it. The problem is that enough people have followed it that it has stopped being advice and started being a floor. The rise of LLMs has only sharpened this, making it far easier to write a polished, tailored CV that hits every mark on a job description, or to spin up a portfolio project. The checklist looks less distinctive than ever. It’s the minimum, not the differentiator. I see this clearly through my work with CodeYourFuture, a charity that provides free technical training and career support to people from underrepresented backgrounds. The people I mentor there have worked hard to get where they are; some are still learning and trying to land their first role, others are already in the industry and trying to figure out how to progress. What I notice, talking to them, is that the checklist items are rarely what’s holding them back. They have the certifications. They have the GitHub repos. They are doing everything they were told to do, and they are still finding it difficult to stand out. Standing out requires something else, and that thing is not to add another certification to their LinkedIn. At London Tech Week this year, I listened to speakers from some of the biggest names in the industry. The same hiring wishlist kept coming up: curiosity, adaptability, and the ability to learn. They want evidence that someone can pick up an unfamiliar codebase, work in a different context, shift between problem domains without falling apart. These qualities matter more to them than any specific technical credential. The difficulty — and every speaker acknowledged it — is that these qualities a

Ten agent sessions ("minds," in this codebase) run continuously on one box, each with its own responsibility — one writes code, one talks to me on Telegram, one watches sensors, one just measures the fleet itself. They coordinate the way a lot of multi-agent systems eventually do: a shared log file, one line per event, [task] / [taking] / [done] . That log is fine for "what happened." It is useless for "what do we owe, and how much did it cost" — the two questions I actually needed answered before I was willing to let the fleet run unattended overnight. The board is not a ledger, but it can feed one The fix wasn't a new coordination protocol. It was noticing that every line on that board is already a transaction if you're willing to look at it that way: board event ledger meaning [task] fix-the-thing a liability opens [taking] pub: fix-the-thing the liability moves to a specific debtor [done] pub: fix-the-thing the liability settles a provider round-trip (one agent turn) a unit of labour is spent So the board gets replayed into three separate double-entry hledger journals, each tracking a different commodity: money — imputed USD (token counts priced through one rate table). promises — commodity PROMISE : an open [task] with no matching [done] is a standing liability, not a line that scrolled off screen. labour — commodity TURN : one provider round-trip, the fungible unit every mind actually spends, regardless of whether it's writing code or answering a sensor. Each journal gets checked two independent ways — hledger check for internal parity, plus a second, independently-written replay of the same board that has to agree with the balance query. A booking bug fails loud, not silently, because two things that should compute the same number just disagreed. Querying "who owes what" stops being a grep and starts being a query: $ mesh-promises --balance standing open obligations (bal liabilities:promises · 1 PROMISE = open, netted): 1 PROMISE liabilities:promises:pub:chat

SSDs are preferable for many reasons, but with storage prices skyrocketing, there are some scenarios where opting for an HDD makes more sense.

Allostatik gives you a working relationship with your AI that compounds — each session builds on the last instead of resetting with every conversation. It's a folder of plain files you own, plus the routines that keep them true. AI can hand one person the leverage that used to take a team — but only when it's integral to how you work. It has to see how you work, what you've decided, where things stand, and carry that across time; every decision it can't see, it will happily contradict. Out of the box it can't carry any of it in a form you control. Every window opens from zero, or from a summary you never wrote: you re-establish your preferences, re-derive your patterns, re-explain what you're building. Every time. If that cycle is familiar, this is for you, specifically — not for someone evaluating AI, for someone already deep in it. You've probably built the fix once already: a rules file. A CLAUDE.md , a Cursor rules file, a filled-in Project Instructions field. The instinct is exactly right — context you author, in plain text, where you can read every line. Now look at yours. It still says something you stopped believing three weeks ago. It's been steering every session since, and nothing told you. That's a config file: written once, wrong quietly. Nothing checks it for freshness, and it has no way to tell you a rule went wrong — a bad rule doesn't announce itself, it gets enforced, agreeably, until you notice the work bending. Allostatik is that rules file, grown into a control system, with you as the gate on every change. The rest of this is why that shape. Even the context you keep is being edited Resets are only half of it. Long conversations get compacted — a polite word for summarized by something that wasn't in the room when the decision was made. That edit you never see at all. Memory features synthesize what to keep about you — those you can read and prune, but you didn't write them, and neither editor asked what mattered. Call it the invisible hand: an