Adam Conway writes that running the Qwen 3.6 27B large language model locally on the Tines 3B platform demonstrated that context window constraints, not model capability, were the primary bottleneck in vibe coding. Despite never encountering the platform's specific architecture or documentation, the model successfully constructed a multi-step web application that combined RSS feeds, correctly following novel platform conventions and autonomously debugging its own output. The author notes that while the model occasionally stalled or lost progress due to a 100,000-token limit that maxed out his GPU's VRAM, it ultimately reasoned its way through complex architectural flaws and timeout issues by iteratively testing and refactoring code, proving highly capable when paired with attentive human oversight.
- Tines 3B injects API credentials through an external proxy, ensuring they never touch the generated code or the model's context window.
- The experiment ran Qwen 3.6 27B on a local Radeon RX 7900 XTX via llama.cpp with multi-token prediction, yielding 40-50 tokens per second.
- Platform behavior was governed by a 4,586-word AGENTS.md rulebook defining Docker volume modes, routing syntax, and cron configurations.
- Context overflow forced manual session forks, causing the model to lose previously verified fixes and inadvertently overwrite functional cache data during timeout retries.
- Tines 3B injects API credentials through an external proxy, ensuring they never touch the generated code or the model's context window.
- The experiment ran Qwen 3.6 27B on a local Radeon RX 7900 XTX via llama.cpp with multi-token prediction, yielding 40-50 tokens per second.
- Platform behavior was governed by a 4,586-word AGENTS.md rulebook defining Docker volume modes, routing syntax, and cron configurations.
- Context overflow forced manual session forks, causing the model to lose previously verified fixes and inadvertently overwrite functional cache data during timeout retries.
The author distinguishes between vibe coding, a reckless approach where developers prompt and accept AI output without review, and agentic engineering, a disciplined professional workflow. While vibe coding is useful for rapid prototyping and MVPs, it lacks the rigor required for scalable or secure systems. Agentic engineering involves orchestrating AI agents under strict human oversight, treating them as fast but unreliable junior developers who require architectural direction and relentless testing.
Key points:
- Distinction between vibe coding (prototyping) and agentic engineering (professional discipline).
- The importance of design docs, rigorous code reviews, and comprehensive test suites in AI workflows.
- How AI-assisted development rewards strong engineering fundamentals rather than replacing them.
- The risk of skill atrophy among junior developers who rely on prompting without understanding underlying principles.
Key points:
- Distinction between vibe coding (prototyping) and agentic engineering (professional discipline).
- The importance of design docs, rigorous code reviews, and comprehensive test suites in AI workflows.
- How AI-assisted development rewards strong engineering fundamentals rather than replacing them.
- The risk of skill atrophy among junior developers who rely on prompting without understanding underlying principles.
This article examines how "vibe coding" – using LLMs to rapidly generate custom software – is transforming sensemaking and data visualization. Previously, bespoke tools demanded significant engineering resources or platform knowledge.
However, the emergence of AI has lowered these barriers, allowing users to create "disposable" interactive tools tailored to specific research tasks.
This empowers non-experts as "directors of design," but the author cautions against mindless trial-and-error, emphasizing the difference between exploratory tools for finding truth and classic visualizations for explaining it.
However, the emergence of AI has lowered these barriers, allowing users to create "disposable" interactive tools tailored to specific research tasks.
This empowers non-experts as "directors of design," but the author cautions against mindless trial-and-error, emphasizing the difference between exploratory tools for finding truth and classic visualizations for explaining it.
Simon Willison explores "vibe coding" - building macOS apps with SwiftUI using large language models like Claude Opus 4.6 and GPT-5.4, without extensive coding knowledge. He successfully created two apps, Bandwidther (network bandwidth monitor) and Gpuer (GPU usage monitor), demonstrating the potential of this approach. The process involved minimal prompting and iterative development, leveraging the LLMs' capabilities for both code generation and feature suggestions.
While acknowledging the need for caution regarding the apps' accuracy, Willison highlights the efficiency and accessibility of building macOS applications in this manner.
While acknowledging the need for caution regarding the apps' accuracy, Willison highlights the efficiency and accessibility of building macOS applications in this manner.
Simon Willison’s annual review of the major trends, breakthroughs, and cultural moments in the large language model ecosystem in 2025, covering reasoning models, coding agents, CLI tools, Chinese open‑weight models, image editing, academic competition wins, and the rise of AI‑enabled browsers.
An analysis of the current LLM landscape in 2026, focusing on the shift from 'vibe coding' to more efficient and controlled workflows for software development and data analysis. The author advocates for tools like AI Studio and OpenCode, and discusses the strengths of models like Gemini 2.5 Pro and Claude Sonnet.
A Python-based log analyzer that uses local LLM (Llama 3.2 to explain the errors in simple language and summarise them (again, in simple language)
Amazon has released Kiro, an AI-powered IDE designed for "vibe coding" that focuses on bringing prototypes into production with features like specs and hooks. It's powered by Claude 4 Sonnet and aims to apply software engineering best practices to the vibe-coding workflow.
A look at everything going on in the world of Replit, including the revamped mobile app, free checkpoints for Agent/Assistant, and the trend of 'vibe coding'.
