MAROKO133 Hot ai: Claude Code costs up to $200 a month. Goose does the same thing for free

📌 MAROKO133 Hot ai: Claude Code costs up to $200 a month. Goose does the same thin

The artificial intelligence coding revolution comes with a catch: it's expensive.

Claude Code, Anthropic's terminal-based AI agent that can write, debug, and deploy code autonomously, has captured the imagination of software developers worldwide. But its pricing — ranging from $20 to $200 per month depending on usage — has sparked a growing rebellion among the very programmers it aims to serve.

Now, a free alternative is gaining traction. Goose, an open-source AI agent developed by Block (the financial technology company formerly known as Square), offers nearly identical functionality to Claude Code but runs entirely on a user's local machine. No subscription fees. No cloud dependency. No rate limits that reset every five hours.

"Your data stays with you, period," said Parth Sareen, a software engineer who demonstrated the tool during a recent livestream. The comment captures the core appeal: Goose gives developers complete control over their AI-powered workflow, including the ability to work offline — even on an airplane.

The project has exploded in popularity. Goose now boasts more than 26,100 stars on GitHub, the code-sharing platform, with 362 contributors and 102 releases since its launch. The latest version, 1.20.1, shipped on January 19, 2026, reflecting a development pace that rivals commercial products.

For developers frustrated by Claude Code's pricing structure and usage caps, Goose represents something increasingly rare in the AI industry: a genuinely free, no-strings-attached option for serious work.

Anthropic's new rate limits spark a developer revolt

To understand why Goose matters, you need to understand the Claude Code pricing controversy.

Anthropic, the San Francisco artificial intelligence company founded by former OpenAI executives, offers Claude Code as part of its subscription tiers. The free plan provides no access whatsoever. The Pro plan, at $17 per month with annual billing (or $20 monthly), limits users to just 10 to 40 prompts every five hours — a constraint that serious developers exhaust within minutes of intensive work.

The Max plans, at $100 and $200 per month, offer more headroom: 50 to 200 prompts and 200 to 800 prompts respectively, plus access to Anthropic's most powerful model, Claude 4.5 Opus. But even these premium tiers come with restrictions that have inflamed the developer community.

In late July, Anthropic announced new weekly rate limits. Under the system, Pro users receive 40 to 80 hours of Sonnet 4 usage per week. Max users at the $200 tier get 240 to 480 hours of Sonnet 4, plus 24 to 40 hours of Opus 4. Nearly five months later, the frustration has not subsided.

The problem? Those "hours" are not actual hours. They represent token-based limits that vary wildly depending on codebase size, conversation length, and the complexity of the code being processed. Independent analysis suggests the actual per-session limits translate to roughly 44,000 tokens for Pro users and 220,000 tokens for the $200 Max plan.

"It's confusing and vague," one developer wrote in a widely shared analysis. "When they say '24-40 hours of Opus 4,' that doesn't really tell you anything useful about what you're actually getting."

The backlash on Reddit and developer forums has been fierce. Some users report hitting their daily limits within 30 minutes of intensive coding. Others have canceled their subscriptions entirely, calling the new restrictions "a joke" and "unusable for real work."

Anthropic has defended the changes, stating that the limits affect fewer than five percent of users and target people running Claude Code "continuously in the background, 24/7." But the company has not clarified whether that figure refers to five percent of Max subscribers or five percent of all users — a distinction that matters enormously.

How Block built a free AI coding agent that works offline

Goose takes a radically different approach to the same problem.

Built by Block, the payments company led by Jack Dorsey, Goose is what engineers call an "on-machine AI agent." Unlike Claude Code, which sends your queries to Anthropic's servers for processing, Goose can run entirely on your local computer using open-source language models that you download and control yourself.

The project's documentation describes it as going "beyond code suggestions" to "install, execute, edit, and test with any LLM." That last phrase — "any LLM" — is the key differentiator. Goose is model-agnostic by design.

You can connect Goose to Anthropic's Claude models if you have API access. You can use OpenAI's GPT-5 or Google's Gemini. You can route it through services like Groq or OpenRouter. Or — and this is where things get interesting — you can run it entirely locally using tools like Ollama, which let you download and execute open-source models on your own hardware.

The practical implications are significant. With a local setup, there are no subscription fees, no usage caps, no rate limits, and no concerns about your code being sent to external servers. Your conversations with the AI never leave your machine.

"I use Ollama all the time on planes — it's a lot of fun!" Sareen noted during a demonstration, highlighting how local models free developers from the constraints of internet connectivity.

What Goose can do that traditional code assistants can't

Goose operates as a command-line tool or desktop application that can autonomously perform complex development tasks. It can build entire projects from scratch, write and execute code, debug failures, orchestrate workflows across multiple files, and interact with external APIs — all without constant human oversight.

The architecture relies on what the AI industry calls "tool calling" or "<a href="https://platform.openai…

Konten dipersingkat otomatis.

🔗 Sumber: venturebeat.com

📌 MAROKO133 Update ai: Prediction Markets Are Sucking Huge Numbers of Young People

Gambling has taken over the country. But it’s not just sports betting that’s emptying wallets. Now, the rise of prediction markets like Polymarket and Kalshi to bet on almost any conceivable outcome, ranging from presidential elections to military interventions to celebrity dramas to the return of our lord and savior Jesus Christ.

The nature of the wagers open up whole new avenues of insider trading and other dishonest practices. Take, for example, the Polymarket bet on the number of tweets Elon Musk will make between February 6 and February 13, which currently has nearly $15 million worth of bets placed. What’s stopping Musk from seeing this and telling a friend how much he’s going to tweet? The answer: practically nothing — hence the numerous scandals that have already emerged over this clear vulnerability.

Just as nefarious is that prediction markets have perfected a wildly addictive formula, especially among young, inexperienced bettors. Part of the appeal is that instead of betting against the house, these platforms claim, you’re betting against other players (though the fine print suggests otherwise). It’s also simpler: either the thing happens, or it doesn’t happen. Adding to that, the prediction markets project credibility through partnerships with news organizations like CNN, which now display their data during broadcasts.

All of this is clearly working. Some users don’t even think of betting as the kind of activity that was once fodder for mob movies and New Hollywood tragicomedies, and more as an investment.

“I wouldn’t describe it as gambling” but a “mix of betting and options trading,” 21-year-old Yadin Eldar, a Florida State University student who’s been betting on prediction markets since 2019, told The Guardian. “It’s not like when you go to the casino, and play against the house, and hope you get to win against the house,” he added. “That’s not what it is.”

Given their freshness in the mainstream, there isn’t as much hard data on prediction markets yet. But one recent analysis found that Polymarket and Kalshi users were losing money faster compared to traditional sports gambling platforms. (The finding was so controversial that Kalshi lashed out by accusing the report of being part of an “extortion plot” before backing down.)

In any case, it’s clear that gambling apps are on an upward trajectory. A study in JAMA Internal Medicine found that online searches for gambling addiction help rose 23 percent between 2018 — the year a court ruling essentially legalized sports betting — and 2023. During roughly that same period, total sports wagers skyrocketed from $4.9 billion to $121.1 billion. An early 2025 survey showed that nearly a quarter of US adults admitted to being sports betting addicts, which was an even higher 37 percent with Gen Z.

And prediction markets in particular appear to be winning over the youth. Another survey from this January showed that millennials and Gen Z were more aware of specific prediction markets like Polymarket and Kalshi than older demographics, while the opposite tended to be true for traditional sports betting platforms.

One 25-year-old former financial risk analyst told NPR he quit his job to bet on Kalshi and Polymarket full time. Another 25-year-old who started his own company to trade on prediction markets told The Wall Street Journal he lost around $100,000 in bets he placed on the Super Bowl. 

“It seems like everything is gambling now, and the appetite for gambling on the most obscure stuff is pretty bonkers,” Danny Funt, author of the new book “Everybody Loses: The Tumultuous Rise of American Sports Gambling,” told Axios. “This is seemingly reaching new levels.”

“This used to be something people did discreetly,” he added. Now, it’s “normalized.”

More on gambling: It Seems Almost Statistically Impossible That This Polymarket Bettor Didn’t Profit Off Inside Knowledge About the Super Bowl Half Time Show

The post Prediction Markets Are Sucking Huge Numbers of Young People Into Gambling appeared first on Futurism.

🔗 Sumber: futurism.com