MAROKO133 Update ai: Inventor of Vibe Coding Admits He Hand-Coded His New Project Wajib Ba

📌 MAROKO133 Update ai: Inventor of Vibe Coding Admits He Hand-Coded His New Projec

Earlier this year, former OpenAI exec Andrej Karpathy coined a new term — “vibe coding” — for using artificial intelligence to rapidly develop software using natural language prompts.

But the approach comes with some glaring shortcomings that have gradually come to light, from major cybersecurity problems leading to mass leaking of sensitive personal information to rampant hallucinations that turn vibe-coded projects into a buggy mess that has to be painstakingly fixed by human programmers.

Even Karpathy himself has seemingly fallen out of love with his own creation. His latest project, dubbed Nanochat, is a “minimal, from scratch” interface that strips down a ChatGPT-like experience to its very basics.

“You boot up a cloud [graphics processing unit] box, run a single script and in as little as four hours later you can talk to your own [large language model] in a ChatGPT-like web UI,” he boasted in a recent tweet.

But as it turns out, the project wasn’t the result of AI vibe coding — it was Karpathy himself.

“It’s basically entirely hand-written,” Karpathy wrote in a followup. “I tried to use Claude/Codex agents a few times but they just didn’t work well enough at all and net unhelpful, possibly the repo is too far off the data distribution.”

In other words, even the godfather of vibe coding doesn’t trust the tech enough to use it on his own project.

To be fair, even Karpathy himself never intended for “vibe coding” to replace human developers in the long run.

“Sometimes the LLMs can’t fix a bug so I just work around it or ask for random changes until it goes away,” he wrote in the February tweet in which he first coined the term. “It’s not too bad for throwaway weekend projects, but still quite amusing.”

But overrelying on the technique can have disastrous consequences as companies continue to cut costs in favor of investing in AI — regardless of Karpathy’s original intentions.

As 404 Media reported last month, a growing number of coders are being tasked with fixing AI-hallucinated code. At best, projects never reach a satisfying level of polish. At worst, the shoddily-put-together lines of code can wipe out entire databases.

Researchers have also found that AI-assisted coding can actually slow down human developers, instead of boosting their productivity.

In a recent report, management consultants Bain & Company found that despite being “one of the first areas to deploy generative AI,” the “savings have been unremarkable” in programming.

“Generative AI arrived on the scene with sky-high expectations, and many companies rushed into pilot projects,” the consultants wrote. “Yet the results haven’t lived up to the hype.”

Content delivery platform Fastly similarly found that at least 95 percent of 800 developers it surveyed had to spend extra time fixing AI-generated code.

Experts have also warned that the trend could result in human coders never learning the ropes properly. Leaning on AI coding too much may be “a bit of an impending disaster” as MIT computer scientist Daniel Jackson told Wired earlier this year.

“Not only will we have masses of broken code, full of security vulnerabilities, but we’ll have a new generation of programmers incapable of dealing with those vulnerabilities,” he added.

More on vibe coding: Amateurs Using AI to “Vibe Code” Are Now Begging Real Programmers to Fix Their Botched Software

The post Inventor of Vibe Coding Admits He Hand-Coded His New Project appeared first on Futurism.

🔗 Sumber: futurism.com

📌 MAROKO133 Update ai: The teacher is the new engineer: Inside the rise of AI enab

As more companies quickly begin using gen AI, it’s important to avoid a big mistake that could impact its effectiveness: Proper onboarding. Companies spend time and money training new human workers to succeed, but when they use large language model (LLM) helpers, many treat them like simple tools that need no explanation.

This isn't just a waste of resources; it's risky. Research shows that AI has advanced quickly from testing to actual use in 2024 to 2025, with almost a third of companies reporting a sharp increase in usage and acceptance from the previous year.

Probabilistic systems need governance, not wishful thinking

Unlike traditional software, gen AI is probabilistic and adaptive. It learns from interaction, can drift as data or usage changes and operates in the gray zone between automation and agency. Treating it like static software ignores reality: Without monitoring and updates, models degrade and produce faulty outputs: A phenomenon widely known as model drift. Gen AI also lacks built-in organizational intelligence. A model trained on internet data may write a Shakespearean sonnet, but it won’t know your escalation paths and compliance constraints unless you teach it. Regulators and standards bodies have begun pushing guidance precisely because these systems behave dynamically and can hallucinate, mislead or leak data if left unchecked.

The real-world costs of skipping onboarding

When LLMs hallucinate, misinterpret tone, leak sensitive information or amplify bias, the costs are tangible.

• Misinformation and liability: A Canadian tribunal held Air Canada liable after its website chatbot gave a passenger incorrect policy information. The ruling made it clear that companies remain responsible for their AI agents’ statements.

• Embarrassing hallucinations: In 2025, a syndicated “summer reading list” carried by the Chicago Sun-Times and Philadelphia Inquirer recommended books that didn’t exist; the writer had used AI without adequate verification, prompting retractions and firings.

• Bias at scale: The Equal Employment Opportunity Commission (EEOCs) first AI-discrimination settlement involved a recruiting algorithm that auto-rejected older applicants, underscoring how unmonitored systems can amplify bias and create legal risk.

• Data leakage: After employees pasted sensitive code into ChatGPT, Samsung temporarily banned public gen AI tools on corporate devices — an avoidable misstep with better policy and training.

The message is simple: Un-onboarded AI and un-governed usage create legal, security and reputational exposure.

Treat AI agents like new hires

Enterprises should onboard AI agents as deliberately as they onboard people — with job descriptions, training curricula, feedback loops and performance reviews. This is a cross-functional effort across data science, security, compliance, design, HR and the end users who will work with the system daily.

1. Role definition. Spell out scope, inputs/outputs, escalation paths and acceptable failure modes. A legal copilot, for instance, can summarize contracts and surface risky clauses, but should avoid final legal judgments and must escalate edge cases.

2. Contextual training. Fine-tuning has its place, but for many teams, retrieval-augmented generation (RAG) and tool adapters are safer, cheaper and more auditable. RAG keeps models grounded in your latest, vetted knowledge (docs, policies, knowledge bases), reducing hallucinations and improving traceability. Emerging Model Context Protocol (MCP) integrations make it easier to connect copilots to enterprise systems in a controlled way — bridging models with tools and data while preserving separation of concerns. Salesforce’s Einstein Trust Layer illustrates how vendors are formalizing secure grounding, masking, and audit controls for enterprise AI.

3. Simulation before production. Don’t let your AI’s first “training” be with real customers. Build high-fidelity sandboxes and stress-test tone, reasoning and edge cases — then evaluate with human graders. Morgan Stanley built an evaluation regimen for its GPT-4 assistant, having advisors and prompt engineers grade answers and refine prompts before broad rollout. The result: >98% adoption among advisor teams once quality thresholds were met. Vendors are also moving to simulation: Salesforce recently highlighted digital-twin testing to rehearse agents safely against realistic scenarios.

4. 4) Cross-functional mentorship. Treat early usage as a two-way learning loop: Domain experts and front-line users give feedback on tone, correctness and usefulness; security and compliance teams enforce boundaries and red lines; designers shape frictionless UIs that encourage proper use.

Feedback loops and performance reviews—forever

Onboarding doesn’t end at go-live. The most meaningful learning begins after deployment.

• Monitoring and observability: Log outputs, track KPIs (accuracy, satisfaction, escalation rates) and watch for degradation. Cloud providers now ship observability/evaluation tooling to help teams detect drift and regressions in production, especially for RAG systems whose knowledge changes over time.

• User feedback channels. Provide in-product flagging and structured review queues so humans can coach the model — then close the loop by feeding these signals into prompts, RAG sources or fine-tuning sets.

• Regular audits. Schedule alignment checks, factual audits and safety evaluations. Microsoft’s enterprise responsible-AI playbooks, for instance, emphasize governance and staged rollouts with executive visibility and clear guardrails.

• Succession planning for models. As laws, products and models evolve, plan upgrades and retirement the way you would plan people transitions — run overlap tests and port institutional knowledge (prompts, eval sets, retrieval sources).

Why this is urgent now

Gen AI is no longer an “innovation shelf” project — it’s embedded in CRMs, support desks, analytics pipelines and executive workflows. Banks like Morgan Stanley and Bank of America are focusing AI on internal c…

Konten dipersingkat otomatis.

🔗 Sumber: venturebeat.com