This weekend, Andrej Karpathy, the previous director of AI at Tesla and a founding member of OpenAI, determined he needed to learn a ebook. However he didn’t wish to learn it alone. He needed to learn it accompanied by a committee of synthetic intelligences, every providing its personal perspective, critiquing the others, and ultimately synthesizing a ultimate reply below the steering of a "Chairman."
To make this occur, Karpathy wrote what he known as a "vibe code mission" — a bit of software program written shortly, largely by AI assistants, meant for enjoyable moderately than perform. He posted the outcome, a repository known as "LLM Council," to GitHub with a stark disclaimer: "I’m not going to help it in any method… Code is ephemeral now and libraries are over."
But, for technical decision-makers throughout the enterprise panorama, trying previous the informal disclaimer reveals one thing way more important than a weekend toy. In a number of hundred strains of Python and JavaScript, Karpathy has sketched a reference structure for probably the most important, undefined layer of the trendy software program stack: the orchestration middleware sitting between company purposes and the unstable market of AI fashions.
As corporations finalize their platform investments for 2026, LLM Council presents a stripped-down take a look at the "construct vs. purchase" actuality of AI infrastructure. It demonstrates that whereas the logic of routing and aggregating AI fashions is surprisingly easy, the operational wrapper required to make it enterprise-ready is the place the true complexity lies.
How the LLM Council works: 4 AI fashions debate, critique, and synthesize solutions
To the informal observer, the LLM Council net utility seems to be nearly similar to ChatGPT. A person sorts a question right into a chat field. However behind the scenes, the applying triggers a complicated, three-stage workflow that mirrors how human decision-making our bodies function.
First, the system dispatches the person’s question to a panel of frontier fashions. In Karpathy’s default configuration, this consists of OpenAI’s GPT-5.1, Google’s Gemini 3.0 Professional, Anthropic’s Claude Sonnet 4.5, and xAI’s Grok 4. These fashions generate their preliminary responses in parallel.
Within the second stage, the software program performs a peer assessment. Every mannequin is fed the anonymized responses of its counterparts and requested to judge them based mostly on accuracy and perception. This step transforms the AI from a generator right into a critic, forcing a layer of high quality management that’s uncommon in normal chatbot interactions.
Lastly, a delegated "Chairman LLM" — at present configured as Google’s Gemini 3 — receives the unique question, the person responses, and the peer rankings. It synthesizes this mass of context right into a single, authoritative reply for the person.
Karpathy famous that the outcomes have been usually shocking. "Very often, the fashions are surprisingly prepared to pick one other LLM's response as superior to their very own," he wrote on X (previously Twitter). He described utilizing the software to learn ebook chapters, observing that the fashions persistently praised GPT-5.1 as probably the most insightful whereas score Claude the bottom. Nonetheless, Karpathy’s personal qualitative evaluation diverged from his digital council; he discovered GPT-5.1 "too wordy" and most popular the "condensed and processed" output of Gemini.
FastAPI, OpenRouter, and the case for treating frontier fashions as swappable elements
For CTOs and platform architects, the worth of LLM Council lies not in its literary criticism, however in its building. The repository serves as a main doc displaying precisely what a contemporary, minimal AI stack seems to be like in late 2025.
The appliance is constructed on a "skinny" structure. The backend makes use of FastAPI, a contemporary Python framework, whereas the frontend is a regular React utility constructed with Vite. Knowledge storage is dealt with not by a fancy database, however by easy JSON recordsdata written to the native disk.
The linchpin of all the operation is OpenRouter, an API aggregator that normalizes the variations between numerous mannequin suppliers. By routing requests by this single dealer, Karpathy averted writing separate integration code for OpenAI, Google, and Anthropic. The appliance doesn’t know or care which firm offers the intelligence; it merely sends a immediate and awaits a response.
This design selection highlights a rising pattern in enterprise structure: the commoditization of the mannequin layer. By treating frontier fashions as interchangeable elements that may be swapped by modifying a single line in a configuration file — particularly the COUNCIL_MODELS checklist within the backend code — the structure protects the applying from vendor lock-in. If a brand new mannequin from Meta or Mistral tops the leaderboards subsequent week, it may be added to the council in seconds.
What's lacking from prototype to manufacturing: Authentication, PII redaction, and compliance
Whereas the core logic of LLM Council is elegant, it additionally serves as a stark illustration of the hole between a "weekend hack" and a manufacturing system. For an enterprise platform workforce, cloning Karpathy’s repository is merely step one in all a marathon.
A technical audit of the code reveals the lacking "boring" infrastructure that industrial distributors promote for premium costs. The system lacks authentication; anybody with entry to the net interface can question the fashions. There isn’t a idea of person roles, that means a junior developer has the identical entry rights because the CIO.
Moreover, the governance layer is nonexistent. In a company atmosphere, sending knowledge to 4 totally different exterior AI suppliers concurrently triggers fast compliance issues. There isn’t a mechanism right here to redact Personally Identifiable Info (PII) earlier than it leaves the native community, neither is there an audit log to trace who requested what.
Reliability is one other open query. The system assumes the OpenRouter API is all the time up and that the fashions will reply in a well timed trend. It lacks the circuit breakers, fallback methods, and retry logic that preserve business-critical purposes working when a supplier suffers an outage.
These absences will not be flaws in Karpathy’s code — he explicitly said he doesn’t intend to help or enhance the mission — however they outline the worth proposition for the industrial AI infrastructure market.
Corporations like LangChain, AWS Bedrock, and numerous AI gateway startups are primarily promoting the "hardening" across the core logic that Karpathy demonstrated. They supply the safety, observability, and compliance wrappers that flip a uncooked orchestration script right into a viable enterprise platform.
Why Karpathy believes code is now "ephemeral" and conventional software program libraries are out of date
Maybe probably the most provocative facet of the mission is the philosophy below which it was constructed. Karpathy described the event course of as "99% vibe-coded," implying he relied closely on AI assistants to generate the code moderately than writing it line-by-line himself.
"Code is ephemeral now and libraries are over, ask your LLM to vary it in no matter method you want," he wrote within the repository’s documentation.
This assertion marks a radical shift in software program engineering functionality. Historically, corporations construct inside libraries and abstractions to handle complexity, sustaining them for years. Karpathy is suggesting a future the place code is handled as "promptable scaffolding" — disposable, simply rewritten by AI, and never meant to final.
For enterprise decision-makers, this poses a troublesome strategic query. If inside instruments could be "vibe coded" in a weekend, does it make sense to purchase costly, inflexible software program suites for inside workflows? Or ought to platform groups empower their engineers to generate customized, disposable instruments that match their actual wants for a fraction of the price?
When AI fashions choose AI: The damaging hole between machine preferences and human wants
Past the structure, the LLM Council mission inadvertently shines a light-weight on a selected threat in automated AI deployment: the divergence between human and machine judgment.
Karpathy’s statement that his fashions most popular GPT-5.1, whereas he most popular Gemini, means that AI fashions might have shared biases. They may favor verbosity, particular formatting, or rhetorical confidence that doesn’t essentially align with human enterprise wants for brevity and accuracy.
As enterprises more and more depend on "LLM-as-a-Decide" programs to judge the standard of their customer-facing bots, this discrepancy issues. If the automated evaluator persistently rewards "wordy and sprawled" solutions whereas human prospects need concise options, the metrics will present success whereas buyer satisfaction plummets. Karpathy’s experiment means that relying solely on AI to grade AI is a method fraught with hidden alignment points.
What enterprise platform groups can study from a weekend hack earlier than constructing their 2026 stack
Finally, LLM Council acts as a Rorschach take a look at for the AI trade. For the hobbyist, it’s a enjoyable approach to learn books. For the seller, it’s a risk, proving that the core performance of their merchandise could be replicated in a number of hundred strains of code.
However for the enterprise know-how chief, it’s a reference structure. It demystifies the orchestration layer, displaying that the technical problem just isn’t in routing the prompts, however in governing the info.
As platform groups head into 2026, many will doubtless discover themselves watching Karpathy’s code, to not deploy it, however to grasp it. It proves {that a} multi-model technique just isn’t technically out of attain. The query stays whether or not corporations will construct the governance layer themselves or pay another person to wrap the "vibe code" in enterprise-grade armor.
