MAROKO133 Update ai: Anthropic launches Cowork, a Claude Desktop agent that works in your

📌 MAROKO133 Hot ai: Anthropic launches Cowork, a Claude Desktop agent that works i

Anthropic released Cowork on Monday, a new AI agent capability that extends the power of its wildly successful Claude Code tool to non-technical users — and according to company insiders, the team built the entire feature in approximately a week and a half, largely using Claude Code itself.

The launch marks a major inflection point in the race to deliver practical AI agents to mainstream users, positioning Anthropic to compete not just with OpenAI and Google in conversational AI, but with Microsoft's Copilot in the burgeoning market for AI-powered productivity tools.

"Cowork lets you complete non-technical tasks much like how developers use Claude Code," the company announced via its official Claude account on X. The feature arrives as a research preview available exclusively to Claude Max subscribers — Anthropic's power-user tier priced between $100 and $200 per month — through the macOS desktop application.

For the past year, the industry narrative has focused on large language models that can write poetry or debug code. With Cowork, Anthropic is betting that the real enterprise value lies in an AI that can open a folder, read a messy pile of receipts, and generate a structured expense report without human hand-holding.

How developers using a coding tool for vacation research inspired Anthropic's latest product

The genesis of Cowork lies in Anthropic's recent success with the developer community. In late 2024, the company released Claude Code, a terminal-based tool that allowed software engineers to automate rote programming tasks. The tool was a hit, but Anthropic noticed a peculiar trend: users were forcing the coding tool to perform non-coding labor.

According to Boris Cherny, an engineer at Anthropic, the company observed users deploying the developer tool for an unexpectedly diverse array of tasks.

"Since we launched Claude Code, we saw people using it for all sorts of non-coding work: doing vacation research, building slide decks, cleaning up your email, cancelling subscriptions, recovering wedding photos from a hard drive, monitoring plant growth, controlling your oven," Cherny wrote on X. "These use cases are diverse and surprising — the reason is that the underlying Claude Agent is the best agent, and Opus 4.5 is the best model."

Recognizing this shadow usage, Anthropic effectively stripped the command-line complexity from their developer tool to create a consumer-friendly interface. In its blog post announcing the feature, Anthropic explained that developers "quickly began using it for almost everything else," which "prompted us to build Cowork: a simpler way for anyone — not just developers — to work with Claude in the very same way."

Inside the folder-based architecture that lets Claude read, edit, and create files on your computer

Unlike a standard chat interface where a user pastes text for analysis, Cowork requires a different level of trust and access. Users designate a specific folder on their local machine that Claude can access. Within that sandbox, the AI agent can read existing files, modify them, or create entirely new ones.

Anthropic offers several illustrative examples: reorganizing a cluttered downloads folder by sorting and intelligently renaming each file, generating a spreadsheet of expenses from a collection of receipt screenshots, or drafting a report from scattered notes across multiple documents.

"In Cowork, you give Claude access to a folder on your computer. Claude can then read, edit, or create files in that folder," the company explained on X. "Try it to create a spreadsheet from a pile of screenshots, or produce a first draft from scattered notes."

The architecture relies on what is known as an "agentic loop." When a user assigns a task, the AI does not merely generate a text response. Instead, it formulates a plan, executes steps in parallel, checks its own work, and asks for clarification if it hits a roadblock. Users can queue multiple tasks and let Claude process them simultaneously — a workflow Anthropic describes as feeling "much less like a back-and-forth and much more like leaving messages for a coworker."

The system is built on Anthropic's Claude Agent SDK, meaning it shares the same underlying architecture as Claude Code. Anthropic notes that Cowork "can take on many of the same tasks that Claude Code can handle, but in a more approachable form for non-coding tasks."

The recursive loop where AI builds AI: Claude Code reportedly wrote much of Claude Cowork

Perhaps the most remarkable detail surrounding Cowork's launch is the speed at which the tool was reportedly built — highlighting a recursive feedback loop where AI tools are being used to build better AI tools.

During a livestream hosted by Dan Shipper, Felix Rieseberg, an Anthropic employee, confirmed that the team built Cowork in approximately a week and a half.

Alex Volkov, who covers AI developments, expressed surprise at the timeline: "Holy shit Anthropic built 'Cowork' in the last… week and a half?!"

This prompted immediate speculation about how much of Cowork was itself built by Claude Code. Simon Smith, EVP of Generative AI at Klick Health, put it bluntly on X: "Claude Code wrote all of Claude Cowork. Can we all agree that we're in at least somewhat of a recursive improvement loop here?"

The implication is profound: Anthropic's AI coding agent may have substantially contributed to building its own non-technical sibling product. If true, this is one of the most visible examples yet of AI systems being used to accelerate their own development and expansion — a strategy that could widen the gap between AI labs that successfully deploy their own agents internally and those that do not.

Connectors, browser automation, and skills extend Cowork's reach beyond the local file system

Cowork doesn't operate in isolation. The feature integrates with Anthropic's existing ecosystem of connectors — tools that link Claude to external information sources and services such as Asana, Notion, PayPal, and other supported partners. Users who have configured these connections in the standard Claude interface can leverage them within Cowork sessions.

Additionally, Cowork can pair with Claude in Chrome, Anthropic's browser…

Konten dipersingkat otomatis.

🔗 Sumber: venturebeat.com

📌 MAROKO133 Eksklusif ai: New flexible artificial neurons can mimic signals and ac

Northwestern University researchers have taken a step toward bridging electronics and the human brain with a new class of printed artificial neurons.

These devices can actively stimulate real brain cells, going beyond just replicating neural behavior in theory.

The study shows that these flexible, low-cost components can generate electrical signals close enough to biological activity to trigger responses in living neurons.

Experiments on mouse brain tissue confirmed that the artificial signals could activate real neural circuits.

The findings point toward future systems that directly interface with the nervous system.

Applications could include brain-machine interfaces and neuroprosthetics for hearing, vision, and movement.

The work also highlights a potential shift toward more energy-efficient computing hardware modeled on the brain.

Rethinking computing hardware

Traditional computing depends on rigid silicon chips filled with identical transistors.

Engineers increase performance by adding more of these components, often at the cost of higher energy use.

The brain operates on a completely different model. It relies on diverse neurons organized in flexible, three-dimensional networks that constantly adapt.

“Silicon achieves complexity by having billions of identical devices,” said Mark C. Hersam, who led the study.

“Everything is the same, rigid and fixed once it’s fabricated. The brain is the opposite. It’s heterogeneous, dynamic and three-dimensional. To move in that direction, we need new materials and new ways to build electronics.”

To move closer to that model, Hersam’s team used printable electronic materials instead of rigid components.

They created inks made from molybdenum disulfide and graphene. Using aerosol jet printing, they deposited these materials onto flexible substrates.

Testing with brain tissue

The team refined a known limitation in printed electronics. Instead of removing stabilizing polymers entirely, they controlled how the material breaks down during operation.

“Instead of fully removing the polymer, we partially decompose it,” Hersam said.

“Then, when we pass current through the device, we drive further decomposition of the polymer.”

This approach creates narrow conductive channels that produce sharp, neuron-like electrical spikes.

The devices can generate a range of signaling patterns, including bursts and continuous firing, similar to biological neurons.

That range allows each artificial neuron to carry more information.

It also reduces the number of components required for complex computation.

To test real-world interaction, the researchers collaborated with neurobiologist Indira M. Raman.

They applied signals from the artificial neurons to slices of mouse cerebellum.

The signals matched key features of natural neural activity and triggered responses in living cells.

“Other labs have tried to make artificial neurons with organic materials, and they spiked too slowly,” Hersam said.

“Or they used metal oxides, which are too fast. We are within a temporal range that was not previously demonstrated for artificial neurons.”

“You can see the living neurons respond to our artificial neuron. So, we’ve demonstrated signals that are not only the right timescale but also the right spike shape to interact directly with living neurons.”

The research also addresses a growing concern of energy consumption in artificial intelligence.

“The world we live in today is dominated by artificial intelligence (AI),” Hersam said.

“The way you make AI smarter is by training it on more and more data.”

“This data-intensive training leads to a massive power-consumption problem. Therefore, we have to come up with more efficient hardware to handle big data and AI.”

By combining brain-like signaling with efficient materials, the study offers a pathway toward computing systems that consume less power while interacting more naturally with biological systems.

The study is published in the journal Nature Nanotechnology.

🔗 Sumber: interestingengineering.com