MAROKO133 Eksklusif ai: A weekend ‘vibe code’ hack by Andrej Karpathy quietly sketches the

📌 MAROKO133 Eksklusif ai: A weekend ‘vibe code’ hack by Andrej Karpathy quietly sk

This weekend, Andrej Karpathy, the former director of AI at Tesla and a founding member of OpenAI, decided he wanted to read a book. But he did not want to read it alone. He wanted to read it accompanied by a committee of artificial intelligences, each offering its own perspective, critiquing the others, and eventually synthesizing a final answer under the guidance of a "Chairman."

To make this happen, Karpathy wrote what he called a "vibe code project" — a piece of software written quickly, largely by AI assistants, intended for fun rather than function. He posted the result, a repository called "LLM Council," to GitHub with a stark disclaimer: "I’m not going to support it in any way… Code is ephemeral now and libraries are over."

Yet, for technical decision-makers across the enterprise landscape, looking past the casual disclaimer reveals something far more significant than a weekend toy. In a few hundred lines of Python and JavaScript, Karpathy has sketched a reference architecture for the most critical, undefined layer of the modern software stack: the orchestration middleware sitting between corporate applications and the volatile market of AI models.

As companies finalize their platform investments for 2026, LLM Council offers a stripped-down look at the "build vs. buy" reality of AI infrastructure. It demonstrates that while the logic of routing and aggregating AI models is surprisingly simple, the operational wrapper required to make it enterprise-ready is where the true complexity lies.

How the LLM Council works: Four AI models debate, critique, and synthesize answers

To the casual observer, the LLM Council web application looks almost identical to ChatGPT. A user types a query into a chat box. But behind the scenes, the application triggers a sophisticated, three-stage workflow that mirrors how human decision-making bodies operate.

First, the system dispatches the user’s query to a panel of frontier models. In Karpathy’s default configuration, this includes OpenAI’s GPT-5.1, Google’s Gemini 3.0 Pro, Anthropic’s Claude Sonnet 4.5, and xAI’s Grok 4. These models generate their initial responses in parallel.

In the second stage, the software performs a peer review. Each model is fed the anonymized responses of its counterparts and asked to evaluate them based on accuracy and insight. This step transforms the AI from a generator into a critic, forcing a layer of quality control that is rare in standard chatbot interactions.

Finally, a designated "Chairman LLM" — currently configured as Google’s Gemini 3 — receives the original query, the individual responses, and the peer rankings. It synthesizes this mass of context into a single, authoritative answer for the user.

Karpathy noted that the results were often surprising. "Quite often, the models are surprisingly willing to select another LLM's response as superior to their own," he wrote on X (formerly Twitter). He described using the tool to read book chapters, observing that the models consistently praised GPT-5.1 as the most insightful while rating Claude the lowest. However, Karpathy’s own qualitative assessment diverged from his digital council; he found GPT-5.1 "too wordy" and preferred the "condensed and processed" output of Gemini.

FastAPI, OpenRouter, and the case for treating frontier models as swappable components

For CTOs and platform architects, the value of LLM Council lies not in its literary criticism, but in its construction. The repository serves as a primary document showing exactly what a modern, minimal AI stack looks like in late 2025.

The application is built on a "thin" architecture. The backend uses FastAPI, a modern Python framework, while the frontend is a standard React application built with Vite. Data storage is handled not by a complex database, but by simple JSON files written to the local disk.

The linchpin of the entire operation is OpenRouter, an API aggregator that normalizes the differences between various model providers. By routing requests through this single broker, Karpathy avoided writing separate integration code for OpenAI, Google, and Anthropic. The application does not know or care which company provides the intelligence; it simply sends a prompt and awaits a response.

This design choice highlights a growing trend in enterprise architecture: the commoditization of the model layer. By treating frontier models as interchangeable components that can be swapped by editing a single line in a configuration file — specifically the COUNCIL_MODELS list in the backend code — the architecture protects the application from vendor lock-in. If a new model from Meta or Mistral tops the leaderboards next week, it can be added to the council in seconds.

What's missing from prototype to production: Authentication, PII redaction, and compliance

While the core logic of LLM Council is elegant, it also serves as a stark illustration of the gap between a "weekend hack" and a production system. For an enterprise platform team, cloning Karpathy’s repository is merely step one of a marathon.

A technical audit of the code reveals the missing "boring" infrastructure that commercial vendors sell for premium prices. The system lacks authentication; anyone with access to the web interface can query the models. There is no concept of user roles, meaning a junior developer has the same access rights as the CIO.

Furthermore, the governance layer is nonexistent. In a corporate environment, sending data to four different external AI providers simultaneously triggers immediate compliance concerns. There is no mechanism here to redact Personally Identifiable Information (PII) before it leaves the local network, nor is there an audit log to track who asked what.

Reliability is another open question. The system assumes the OpenRouter API is always up and that the models will respond in a timely fashion. It lacks the circuit breakers, fallback strategies, and retry logic that keep business-critical applications running when a provider suffers an outage.

These absences are not flaws in Karpathy’s code — he explicitly stated he does not intend to support or improve the project — but they define the value proposition for the commercial AI infrastructure market.

Companies like LangChain, AWS Bedrock, and various AI gateway startups are essentially selling the "hardening" around the core logic that Karpathy demonstrated. They provide the security, observability, and compliance wrappers that turn a raw or…

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🔗 Sumber: venturebeat.com

📌 MAROKO133 Update ai: This Video of a Robot Playing Basketball Is EXTREMELY Impre

It’s one small step for man — and one giant, badass layup for robot kind.

Researchers at the Hong Kong University of Science and Technology (HKUST) have programmed a Unitree G1 humanoid robot to play basketball, almost perfectly mimicking the skills of a human athlete.

A video shared by HKUST PhD student Yinhuai Wang shows the robot dribbling, taking jump shots, and even pivoting on one of its feet to evade the student’s attempts to block it from taking a shot.

Wang called it the “first-ever real-world basketball demo by a humanoid robot,” boasting that he “became the first person to record a block against a humanoid.”

It’s an impressive demo, showcasing how far humanoid robotics has come in a matter of years. Unitree, in particular, has stood out in an increasingly crowded field, with its G1 rapidly picking up new skills.

We’ve seen the four-foot-four-inch humanoid perform impressive kung fu moves and easily shrug off a direct flying dropkick from an adult human. We’ve even seen two of them take each other on in a head-to-head kickboxing contest.

Wang and his colleagues are teaching robots how to play basketball through a system they’ve dubbed “SkillMimic,” which is described on his website as a “data-driven approach that mimics both human and ball motions to learn a wide variety of basketball skills.”

“SkillMimic employs a unified configuration to learn diverse skills from human-ball motion datasets, with skill diversity and generalization improving as the dataset grows,” the writeup continues. “This approach allows training a single policy to learn multiple skills, enabling smooth skill switching even if these switches are not present in the reference dataset.”

While netizens were generally impressed by the robot’s basketball skills, others were a little more skeptical.

“Love that the programmer focused on showboating rather than fundamentals,” one wrote.

“Robots will do everything but fill the dishwasher,” another joked.

Others imagined a future in which bipedal robots dominate sports.

“Man, I hope I get to see proper robotics basketball leagues,” another Reddit user mused.

More on the Unitree G1: Unstoppable Martial Arts Robot Can Take a Direct Dropkick Without Falling Down

The post This Video of a Robot Playing Basketball Is EXTREMELY Impressive appeared first on Futurism.

🔗 Sumber: futurism.com