Jonathan Pedoeem

We hosted the first Vibe Coding Olympics

Last week, we hosted the first-ever Vibe Coding Olympics in the heart of New York City: a three-round, aggressively time-boxed hackathon where the deciding score was whether what teams shipped felt good to use. Prompting used to be the hard part. Now the hard part is deciding

Multi-agent collaboration via evolving orchestration

A NeurIPS 2025 paper introduces dynamic orchestration where a central "puppeteer" learns to route tasks between agents based on evolving problem states, outperforming fixed multi-agent pipelines.

Prompt Repetition Improves Non-Reasoning LLMs: Google's New Study

Google researchers found that simply repeating your prompt—copying and pasting it twice—dramatically improves LLM accuracy on non-reasoning tasks, with gains up to 76% and zero performance degradation.

Benchmarking Gemini 3.1 Pro: Latency, cost, and reasoning trade-offs

Google's Gemini 3.1 Pro represents a meaningful step forward for developers building applications that require advanced reasoning. Announced in February 2026, the model is positioned around stronger problem-solving without forcing teams to pay more for the privilege. At PromptLayer, where teams manage prompts and evaluate model

How do you observe LLM systems in production?

Deploying LLMs is only half the battle — once live, they can hallucinate, drain budgets, or slow down in ways standard monitoring never catches. LLM observability connects inputs, outputs, latency, cost, and quality into a single picture.

Super Claude Code: How structured prompts turn Claude Code into a true development partner

AI coding assistants have become genuinely useful, but getting consistent, expert-level output from them remains surprisingly tricky. Developers struggle with the gap between an LLM's raw potential and its actual performance on complex coding tasks. SuperClaude, a community-built framework created by developer Anton Knorery, addresses this

Claude-opus-4-1-20250805-thinking-16k: What the Thinking-16k label actually means for your workflows

Claude Opus 4.1 arrived on August 5, 2025, and with it came a naming convention that caused some confusion. claude-opus-4-1-20250805-thinking-16k is not a separate base model; it is a specific reasoning-budget configuration of Anthropic's flagship model. Understanding that distinction can

Is Opus smarter than Sonnet? Opus vs. Sonnet

The question of which AI model is "smarter" depends on what you need that intelligence to do. At PromptLayer, we spend a lot of time comparing how different models behave across real prompts, agents, and production workflows. Both models come from Anthropic's Claude family, but they

Prompt routers and flow engineering: building modular, self-correcting agent systems

The shift from crafting individual prompts to designing entire reasoning flows has fundamentally changed how we build AI applications. The PromptLayer team have watched this evolution closely, observing how teams move from trial-and-error prompt tweaking toward systematic architectures that can catch their own mistakes. This transition represents more

We hosted the first Vibe Coding Olympics

Multi-agent collaboration via evolving orchestration

Prompt Repetition Improves Non-Reasoning LLMs: Google's New Study

Benchmarking Gemini 3.1 Pro: Latency, cost, and reasoning trade-offs

How do you observe LLM systems in production?

Super Claude Code: How structured prompts turn Claude Code into a true development partner

Claude-opus-4-1-20250805-thinking-16k: What the Thinking-16k label actually means for your workflows

Is Opus smarter than Sonnet? Opus vs. Sonnet

Prompt routers and flow engineering: building modular, self-correcting agent systems

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