Insights on RL-driven agent optimization, AI infrastructure, and the future of autonomous systems.
We investigate whether prompt optimization methods can teach LLMs to perform analog circuit placement, achieving 97% of expert quality with no training data — outperforming RL fine-tuning of a 120B model by 26%.
We hand an LLM a 9-line random-search stub and five rounds of contrastive feedback later, it produces a specialized solver that matches or outperforms Optuna on 96% of all 55 EvalSet benchmarks — with no human-written solver code.
We tested three prompt optimizers across five benchmarks. No single method won everywhere. Here's the decision framework — and why the right choice changes everything.
The sequel to PromptGrad. We built an optimizer that watches AI fail, then succeed, on the same problem — and extracts the reasoning pattern. +29% on multi-hop reasoning, +18% on GDPR compliance, +19% normalized across six benchmarks.
A BBC reporter watched a security researcher take over her machine live on air through a vibe-coding platform. That same week, a DeFi protocol lost $1.78M to an AI-written smart contract. These aren't anomalies — they're the thesis.
We applied gradient descent principles to natural language prompts and beat the state-of-the-art optimizer by 18%. Here's how — and why it changes the economics of LLM deployment.
We ran 85 autonomous AI tasks to build an enterprise CLM application. Here are the 8 architectural lessons, the build loop checklist, and the 60/40 budget rule we learned the hard way.
We let an autonomous AI build an enterprise CLM app — 85 tasks, 16 hours, zero human intervention. Then I tried to log in. Here's the chronological war story of every bug I found, in order, with timestamps.
We reverse-engineered the scaffolding behind Anthropic's Claude-built C compiler and ran our own experiments to explore scaling laws for parallel coding agents.
I gave an AI agent a crystal-clear PRD and full autonomy to build a DocSend clone. Five days later, I had a bloated EBS bill, broken cookies, and hallucinated API parameters. Here's what went wrong — and what it means for enterprise AI.
Standard AI models optimize for average outcomes—but average is invisible in the attention economy. Learn how Distributional RL beats frontier models at viral content generation while cutting costs by 99%.
Multi-objective reinforcement learning (MORL) enables AI agents to optimize multiple conflicting goals simultaneously. Learn how MORL works, when to use it, and how it's transforming production AI systems.
Learn how Reinforcement Fine-Tuning (RFT) enables AI models to reason through complex problems instead of just memorizing answers—and discover if your enterprise use case is ready for it.
How we used Reinforcement Learning to increase retargeting conversions by 44% without risking brand safety.
Discover four counter-intuitive techniques from Multi-Objective Reinforcement Learning that go beyond prompt engineering to build robust, adaptable AI agents.
Understanding the AI agent ecosystem through a simple analogy: LLMs are engines, agents are cars, and Vizops is the precision tuning that makes them run efficiently.
Exploring the Agent-as-Infrastructure paradigm and why it demands a fundamentally different approach to optimization and operations.
Discover how Vizops uses reinforcement learning to continuously optimize AI agents, moving beyond static prompt engineering to dynamic, adaptive intelligence.