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Building Educational Software for Mandarin Chinese and Interlingua IALA

Building Educational Software for Mandarin Chinese and Interlingua IALA Language-learning software is most useful when it makes structure visible. I’m Ian Blas, a developer based in Buenos Aires, Argentina, and I build educational tools around Mandarin Chinese, Interlingua IALA, etymology, morphology, writing systems, and open-source language learning. Two projects, one educational approach My work currently takes two complementary forms. Chety is an educational app for Mandarin Chinese. It approaches characters and words through their structure, etymology, morphology, historical development, and use in context. Schola Interlingua is a free, open-source learning platform for Interlingua IALA. It brings together lessons, readings, review tools, and progress-oriented study on multiple platforms. The languages are different, but the design question is similar: how can software help a learner notice the patterns that make a language readable and memorable? Learning through structure For Mandarin Chinese, a character is not only a unit to memorize. It can open a path into components, historical forms, pronunciation, word formation, and reading. That perspective guides Chety’s tools for exploring characters and vocabulary. For Interlingua IALA, the focus shifts toward transparent vocabulary, reading, morphology, and sustained practice. Schola Interlingua is designed to make that learning path approachable without separating learners from the materials and tools that support it. In both projects, the goal is practical: make language learning more legible. Etymology and morphology are useful when they give learners better ways to connect forms, meanings, and usage. An open educational practice I care about software that can be examined, shared, and improved. Schola Interlingua’s development is available through its GitHub repository , and my broader work can be found on GitHub . I also write and share updates through Medium and Substack . Explore the projects Chety — Chines

2026-07-10 原文 →
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3 Key Advantages of Cloudflare Tunnel for Self-Hosted Services

A few years ago, securely accessing my servers at home or in a small office from outside was always a headache. Dynamic IP addresses, complex port forwarding settings in the modem interface, and even the need to open rules in the network firewall always presented me with a new problem. Especially outside of corporate projects, when setting up my side products or test environments, these processes caused a loss of time. Cloudflare Tunnel offered a game-changing solution for such self-hosted services. Essentially, a cloudflared daemon (tunnel client) on your local network establishes a single outbound connection to Cloudflare, eliminating the need to open any firewall holes for incoming connections. This model provides significant advantages over traditional methods in terms of both security and ease of use. Why Were Traditional Self-Hosting Methods Challenging? Exposing a self-hosted service to the internet typically brings multiple technical challenges. First, most home or office internet connections have dynamic IP addresses, meaning your IP address changes from time to time. In this case, to access your service via a fixed domain name, you need to resort to solutions like Dynamic DNS (DDNS), which adds an extra dependency and sometimes causes delays. Second, and perhaps most importantly, is the necessity of opening port forwarding rules in your network firewalls (modem/router firewall). This means you need to direct specific ports to your internal IP address to allow incoming connections. This process both increases security risks (internet-facing ports invite brute-force attacks) and causes access problems if not configured correctly. I once experienced a serious panic during a client project when I accidentally exposed a critical internal service port to the outside while dealing with these ports. Since that day, I approach such manual interventions with more skepticism. ℹ️ Port Forwarding Risks Every port you open in your network firewall creates a potential at

2026-07-08 原文 →
AI 资讯

Local LLM Setup with Ollama: Keep Your Data Secure

When analyzing supply chain data in a production ERP, the idea of sending critical information to a publicly accessible cloud-based LLM always bothered me. Processing sensitive data like proprietary production plans, customer lists, or financial details on third-party servers was an unacceptable risk for me. In such situations, the only way to ensure data security is to run the LLM in a local environment, under our own control. This is where Ollama comes in. Ollama is a tool that allows you to easily run large language models (LLMs) on your local system. This way, you can set up your own AI assistant without an internet connection and without the risk of leaking your data, securely automating your sensitive business processes. In this post, I will step-by-step explore why Ollama is important, how to install and use it, and the data security advantages it offers. Why Ollama is Important: Does it Provide Data Security and Control? The potential of LLMs in modern software development processes and operational workflows is huge, but the privacy and security of corporate data are always a primary concern. Especially in high-security environments like a bank's internal platform or in the financial calculators of my own side product, I need to process user data without sending it externally. In these scenarios, cloud-based LLM services often create a new risk factor rather than a solution. Ollama is designed to close this critical data security gap. By running models on your local machine, it prevents your data from leaving your company network or personal computer. This is a vital advantage, especially for those working in sectors subject to data protection regulations like GDPR and KVKK. Furthermore, because it doesn't require an internet connection, you can continue to benefit from LLM capabilities even in offline environments or during network outages. Since you have full control, you can manage from start to finish which model runs with which data, how much resource t

2026-07-08 原文 →