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Harsh Mishra

As seen in: Medium, Memory Studies, Taylor & Francis Online, DEV Community, NRC Handelsblad, Amar Ujala, Mondaq, The Cureus Journal of Medical Science, Youth Ki Awaaz, Analytics Vidhya and

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Articles

Loop Engineering for AI Agents: How /loop is Changing AI Workflows

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By Harsh Mishra
| Analytics Vidhya
AI agents are moving from one-time assistants to persistent workers that can repeat tasks, monitor changes, run checks, update workflows, and return with results. Instead of prompting an LLM once and deciding every next step manually, teams can now use AI agents that keep working (on a Loop) until a goal or stop condition is met. This matters because real work is rarely a single prompt.
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Sakana Fugu: Multi-Agent System as a Model

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By Harsh Mishra
| Analytics Vidhya
Reading list For years, AI progress has centered on scaling individual foundation models: larger parameters, longer context windows, stronger reasoning, and better tool use. Sakana AI’s Fugu points elsewhere, behaving like one model from the outside while coordinating multiple expert agents internally. A single API call can trigger direct answering, specialist delegation, intermediate verification, and final synthesis, hiding orchestration complexity behind a normal LLM interface.
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DiffusionGemma: Google’s Diffusion-Based Open Model for Faster Text Generation

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By Harsh Mishra
| Analytics Vidhya
Large language models usually generate text one token at a time. While this autoregressive approach delivers strong quality and instruction following, it can be inefficient for local users because GPUs often spend more time moving weights from memory than doing parallel compute. Google DeepMind’s DiffusionGemma takes a different path, generating and refining blocks of tokens in parallel using diffusion-style text generation.
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