by HDDH
A software engineer with 20 years of experience documenting no-code app development know-how with Genspark AI.
Genspark's "AI Chat Mixture-of-Agents" lets you query Claude, Gemini, GPT, and other leading AI models simultaneously — and get a single synthesised, high-quality answer in return. It cuts the manual "AI hopping" workflow to roughly 1/5 the time. We explain why only Genspark — a platform with no AI model of its own — could build this, and how to use it effectively.
Claude Code is a powerful AI coding tool — but it has no built-in image generation or media processing capabilities. Left to its own devices, it resorts to free services that produce obviously AI-looking results. Install Genspark CLI alongside it and those blind spots disappear: Claude Code can now call gsk img, gsk transcribe, and more to deliver professional-quality output it couldn't produce alone.
AI image generation is reshaping frontline sales. In this post, I walk through a 3-step workflow — smartphone photo, instant AI rendering, on-the-spot quote — that lets sales reps deliver a photorealistic completion image and a ballpark figure during the pitch itself. Includes real implementation challenges and how AI agents can automate the follow-up.
Genspark's AI chat has added three new models: DeepSeek V4 Pro (China), Trinity Large Thinking (USA · Arcee AI), and Minimax M2P7 (China · MiniMax). This post breaks down each model's background, online reception, and real-world performance — including an honest look at Japanese language support and what each one is actually best suited for.
I'm laying out my entire AI subscription stack as of May 2026 — what I pay for, what I canceled, and what my costs look like after trimming the fat. Spoiler: Claude + Genspark as the core two, Cloudflare for $5/month infrastructure, and cutting ChatGPT, Perplexity, and GitHub Copilot saved around $40–50/month.
An in-depth comparison of Kintone's no-code and Genspark's AI programming. Kintone is codeless and secure, while Genspark handles everything from sandbox to deployment using natural language. We will clearly explain each of their "debugging walls" and the criteria for choosing between them.
We explain 5 prompting techniques for extracting highly accurate code from Genspark. We will practically introduce them, covering explicitly stating the version, utilizing personalization features, specifying official documentation, structured debugging templates, and comprehensive prompts for Sparkpage.
A thorough comparison of Genspark and Perplexity from an engineering perspective. Perplexity excels at rapid fact verification with its Sonar engine, while Genspark specializes in complex research by utilizing Sparkpage and switching between multiple AI models. We will explain how to distinguish their usage in development settings.
This explains the essential security settings required to safely use Genspark in a corporate environment. We have compiled specific measures to prevent information leakage risks, including steps to turn off AI data retention, prohibiting the input of confidential information, and local processing of customer data.
Honest review of the Genspark for Word add-in from Genspark 4.0. Natural language instructions work well for formatting tasks, but the current version is less polished than the PowerPoint and Excel add-ins. Looking forward to future improvements.
Hands-on review of the Genspark for Excel add-in from Genspark 4.0. Unlike the browser version, you work directly in Excel with a side chat panel. Generated Gaussian data, histograms, and statistical analysis all with simple chat instructions.
Hands-on review of the Genspark for PowerPoint add-in introduced in Genspark 4.0. Unlike the browser version, it outputs directly to PowerPoint with no export step needed. PDF conversion issues are also resolved — a major upgrade for Microsoft Office users.