且炼时光
且炼时光 is the personal identity and publishing name of linshenkx, an individual developer who describes himself as a 2020 graduate focused mainly on Java big-data development and on designing and implementing things independently. His public work consists of technical notes and open-source software; the flagship current project, Prompt Optimizer, helps people write, optimize, test, evaluate, and reuse AI prompts.
At a Glance
- Individual AI users and prompt engineers
- Software developers and technical teams experimenting with LLMs
- Users of text-generation and image-generation models
- Developers needing local, private or self-hosted prompt tooling
- +1 more
AI Tools by 且炼时光
(1)
Prompt Optimizer
Open Source Prompt Optimizer
Discussions
No discussions yet
Be the first to start a discussion about 且炼时光
Latest News
Prompt Optimizer v2.11.11 released with evaluation-model selection and save-order fixes, feature-model backup/restore improvements, large-image handling and clearer first-use guidance.
Prompt Optimizer v2.11.0 added deployment-aware remote backups, safer complete-resource restore flows, OpenAI multi-image editing support and a Prompt Garden image-to-image guide.
Version 2.10.1 launched a bilingual MkDocs documentation site and updated deployment, navigation and project landing-page materials.
Version 2.10.0 added Prompt Garden discovery/import/favorites flows, standardized prompt model/session structures, expanded complete backups and improved desktop IPC reliability.
Products & Services
Open-source AI prompt optimization software. It optimizes system and user prompts, analyzes and evaluates results, supports iterative improvement, stores reusable prompt assets, and offers web, desktop, Chrome extension, Docker and MCP deployment/use modes. The project website is prompt.always200.com and the source repository is github.com/linshenkx/prompt-optimizer.
Personal technical publishing site covering Java/big-data development, Hadoop/Spark/Flink, Kubernetes, Docker, WSL, Linux/server administration and implementation notes.
Market Position
Prompt Optimizer is positioned as an open-source, multi-platform prompt workbench rather than only a prompt-polishing webpage: it combines optimization, evaluation, testing, prompt assets, image workflows and model configuration, while allowing client-side use or self-hosting. Its differentiators in the inspected materials are the pure-client-side architecture, broad deployment options (web, desktop, Chrome and Docker), and MCP support.
Leadership
Founders
且炼时光 (linshenkx)
Individual developer and creator. His About page says he is a 2020 graduate, primarily engaged in Java big-data development; his public work includes Java, Hadoop/Spark/Flink, Kubernetes, Docker, WSL and AI tooling. GitHub identifies the account as 且炼时光 / linshenkx.
Founding Story
This is not presented as a company or funded venture. The official About page presents 且炼时光 as a personal blog/identity of a developer who likes designing and implementing things, and the site motto is “韶华未褪，且炼时光.” The site began as a place for personal technical notes and projects; Prompt Optimizer is a later open-source project intended to improve prompt quality and turn prompts into reusable assets.
Business Model
Revenue Model
The inspected sources present Prompt Optimizer as an open-source, client-side tool with a free online version and self-hosting options (Vercel, Cloudflare, Docker and desktop downloads). The repository exposes AGPL-3.0 licensing and a voluntary support/donation link; no subscription or usage-based paid plan is described in the sources.
Pricing Tiers
Browser-based Prompt Optimizer at prompt.always200.com; the README recommends it and says application data is stored in the browser while model requests go directly to the configured provider.
The repository provides Vercel, Cloudflare, Docker, Docker Compose, desktop release and Chrome extension routes.
Target Markets
- Individual AI users and prompt engineers
- Software developers and technical teams experimenting with LLMs
- Users of text-generation and image-generation models
- Developers needing local, private or self-hosted prompt tooling
- Users of MCP-compatible AI applications
- Prompt engineering and iterative improvement of system or user prompts
- Testing and evaluating prompt quality across models and outputs
- Reusable prompt-template workflows with variables
- Text-to-image and image-to-image prompt creation
- Multi-turn conversation and tool-calling experiments
- Local/private deployment for developers using commercial APIs or local models such as Ollama