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What I Learned Building a Diabetes Management Website

A few months ago I started building [reversemydiabetes.co]a small health platform focused on helping people manage type 2 diabetes through diet and lifestyle changes. I'm not a doctor — I'm a builder — but the project turned into a genuinely interesting technical challenge, and I wanted to share some of what I learned along the way. Why I started this Type 2 diabetes affects a huge number of people, and a lot of the advice online is either paywalled, badly organized, or written in a way that's hard to act on. I wanted to build something simple: a site that gives people practical, easy-to-follow guidance on blood sugar management, diet planning, and day-to-day habits — without needing a login, a subscription, or a medical degree to understand it. The technical side A few decisions shaped how the site turned out: Content structure over cleverness. Early on I over-engineered the information architecture — trying to build dynamic filtering for every possible diet preference. I scrapped most of it. What actually mattered was clear, well-organized static content: a diabetes diet plan page, a blood sugar basics guide, and a meal-planning section. Simple beats clever when the audience isn't tech-savvy. SEO became a first-class concern, not an afterthought. Health content lives or dies on whether people can actually find it. I spent real time on keyword research — things like "diabetes diet plan," "blood sugar levels," and "type 2 diabetes management" — and restructured pages around what people were actually searching for, rather than what sounded good internally. Performance mattered more than I expected. A lot of the target audience is older, on slower connections, or on older devices. I ended up stripping out a bunch of client-side JavaScript I didn't need and leaned on plain HTML/CSS wherever possible. Lighthouse scores went from "fine" to "actually fast," and bounce rate dropped noticeably. Trust signals are a real UX problem for health content. Unlike a SaaS landing pa

2026-09-04 原文 →
开发者

Continuous glucose monitors are about to get more complicated

This is Optimizer, a weekly newsletter sent from Verge senior reviewer Victoria Song that dissects and discusses the latest gizmos and potions that swear they're going to change your life. Opt in for Optimizer here. My mom was a dramatic woman prone to overreacting. When I was a snotty teen, I told her she needed […]

2026-09-02 原文 →
AI 资讯

My Agent Found Real Improvements. The Statistics Still Killed the Promotion.

Previously: 9 Bugs That All Looked Like a Working System · I Built an AI That Rewrites Its Own Prompts · The Edit That Fixed 4 Tasks and Broke 1 · The Gate Is the Product · The Doctor Who Diagnosed Every Patient · 4 Models, 0 Promotable Edits In v0.1.0, an edit fixed 4 tasks and broke 1. Net +3 on 26 tasks. p=0.23. Gate rejected. The ceiling was clear: if you do not move enough tasks, the gate should say no. In v0.2.0, we expanded the A/B corpus to 40 tasks. We fixed the pipeline bugs. We added rejection context. We tested stronger models. The math got cleaner, not kinder. The ceiling shifted. It did not disappear. The v0.1.0 Result: 26 Tasks, 5 Movable, p=0.23 The edit was real. It fixed 4 tasks and broke 1: Task Prompt A Prompt B Expected Change classify-015 technical urgent urgent FIXED classify-023 security urgent, security urgent, security FIXED classify-024 feature feature, billing feature, billing FIXED classify-029 feature other other FIXED classify-014 technical feature technical BROKEN Net: +3. Mean delta = 0.115. p=0.23. The permutation test computes this by shuffling task labels 1,000 times and counting how often random chance produces a delta ≥ 0.115. 23% of the time — above the 5% threshold. The sign-test floor with 5 discordant pairs out of 26 is ~0.031 one-sided. Even a flawless edit that fixed all 5 would barely clear p<0.05 two-sided. The v0.2.0 Result: 40 Tasks, Still Nothing We expanded to 40 tasks. We should have more power. Here's what Mistral 24B, our strongest analyzer, produced: Iter p-value Mean delta Accuracy 1 0.55 +0.025 64% 2 0.52 +0.025 64% 3 1.0 0.0 64% 4 0.52 +0.025 64% 5 0.77 -0.025 64% Mistral produced positive deltas in 3 of 5 iterations. That is real signal. But the delta is +0.025 — 2.5% improvement on 40 tasks. At p~0.5, there is roughly a coin-flip chance this is noise. The ceiling did not disappear. It moved: with 40 tasks, the sign-test floor for a flawless edit that moves 5 tasks is ~0.016 one-sided — clearable. But Mistral

2026-09-02 原文 →
AI 资讯

Dyson made a camera-equipped toothbrush that flosses for you

Dyson is once again expanding its line of personal care products with a device that focuses on your teeth instead of your hair. As the name implies, the $499 CameraJet is the first electric toothbrush to incorporate a camera into the brush head. Available starting today in ceramic blue or ceramic pink color options, it […]

2026-09-01 原文 →