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http://i.stanford.edu/pub/cstr/reports/cs/tr/68/85/CS-TR-68-...

Clicking on the links now reveals blank pages and empty PDFs. "Intellectually, it’s not acceptable.”

Why Semantic HTML is a Superpower for Your Website As I’ve been building my personal portfolio during the IYF Season 11 program, I realized that writing code is about more than just making things look "right"—it’s about making them accessible for everyone. The secret is Semantic HTML. What is Semantic HTML? Semantic HTML uses tags that provide meaning to the web page rather than just layout instructions. Tags like , , , , and tell the browser and screen readers exactly what content they are interacting with. If you use for everything, you are handing a screen reader a blank map. Semantic tags act like a GPS, allowing users with visual impairments to navigate your site efficiently. Before vs. After The difference is clear when comparing structure: Before (Non-Semantic): HTML My Portfolio Home | About | Contact Welcome to my site... After (Semantic): HTML My Portfolio Home About Contact Welcome to my site... Fixing Accessibility Issues During my accessibility audit (Task 2.3), I identified three key areas to improve: Missing alt Attributes: I had images without descriptions. I fixed this by adding context, such as alt="Portrait of Gladwell Muthoni, web development student", ensuring screen readers can describe images to visually impaired users. Lack of Semantic Structure: My initial gallery relied on generic containers. Switching to and tags organized my project list logically, helping assistive technology categorize the content. Heading Hierarchy: I was using tags for visual styling rather than logical structure. I corrected this to follow a strict to hierarchy, which helps screen readers outline the page properly. My portfolios URL https://github.com/gladwellmuthoni/iyf-s11-week-01-gladwellmuthoni.git

TL;DR — Claude Code sends its requests wherever one environment variable points. Aim that at a small local translator and it runs on the Kiro plan you already pay for. Full setup below, plus the two snags worth knowing about. Claude Code and Kiro have something funny in common: underneath, they're powered by the same Claude models. Same brain. They just grew up speaking different dialects, so out of the box they can't hold a conversation. I noticed this right as I was about to start a second subscription for Claude Code. My Kiro plan was already renewing every month, already serving the exact models Claude Code wanted to charge me for again. Paying twice to talk to the same thing felt absurd. So instead of buying a second seat, I hired an interpreter. One small program that sits between them, listens to Claude Code, and relays everything to Kiro in a dialect it understands. Here's how to set it up, and what I learned doing it. Why they can't just talk Claude Code is more open-minded than people assume. It doesn't hard-code where it sends requests. It reads one environment variable, ANTHROPIC_BASE_URL , and ships everything to that address. Normally that's the official endpoint, but it'll happily send its requests anywhere you tell it to. That's the opening. Point it somewhere local and the whole thing becomes possible. Meet the interpreter The catch is that Claude Code and Kiro phrase things differently. You can't just redirect one at the other and expect them to understand each other. You need a translator fluent in both. That's kiro-gateway-next : a tiny proxy that runs on your own machine. A request arrives phrased one way and leaves phrased another: Claude Code (phrases it for Anthropic) ──▶ kiro-gateway (rephrases it for Kiro) ──▶ your Kiro account Claude Code gets a reply in the format it expects. Kiro receives a request it recognizes. The interpreter does the rephrasing in the middle, and the conversation just flows. Setting it up, step by step Six steps. Abo

You submitted your extension, waited days for review, and got back a rejection with a violation called "Purple Potassium." Your extension looks fine to you, so what does it even mean? Here is what it is, why it happens, and how to catch it before you ever hit submit. What "Purple Potassium" actually means "Purple Potassium" is Google's internal tag for excessive or unused permissions . Your manifest requests access to something your code does not actually use, and the reviewer flags it. It is one of the most common reasons a Chrome extension gets rejected, and it is frustrating precisely because the extension works fine in testing. Review is checking something testing never does: whether every permission you ask for is justified by your code. The usual causes 1. API permissions you declared but never call. You added tabs , bookmarks , or cookies to your manifest at some point, but there is no chrome.bookmarks.* call anywhere in your code. 2. Host access that is too broad. You requested <all_urls> when your extension only touches one site: // Flagged "host_permissions" : [ "<all_urls>" ] // Better "host_permissions" : [ "https://*.example.com/*" ] Leftover permissions after removing a feature. You shipped a feature that needed downloads, later removed the feature, and forgot to remove the permission. The tabs misunderstanding. The tabs permission does not grant access to the tabs API. Basic methods like chrome.tabs.create() work without it. It only grants four sensitive Tab properties: url, pendingUrl, title, and favIconUrl. If you declare tabs but never read those, it counts as unused. How to fix it by hand List everything in permissions, optional_permissions, and host_permissions. For each one, search your code for the matching chrome. call. Remove any permission with no usage. Narrow and other broad patterns to the specific hosts you need. In your reviewer notes, write one plain sentence per sensitive permission explaining why you need it. Reviewers often lack con

The biggest challenge wasn't choosing a language model or designing prompts—it was managing context over time. Once an application grows beyond isolated conversations, memory becomes just as important as reasoning. An assistant that remembers previous architectural decisions, coding preferences, and project history can contribute much more effectively than one that starts from scratch every session. Runtime intelligence proved to be equally important. Not every request deserves the same computational resources. Routing tasks based on complexity, enforcing execution budgets, and maintaining an audit trail make AI systems more predictable and practical for real-world development. DevPilot AI brings these ideas together by combining Google Gemini for reasoning, Hindsight for persistent memory, and cascadeflow for runtime intelligence. While the project will continue to evolve, building it reinforced one idea above all else: the future of AI applications isn't just about generating better responses. It's about building systems that can remember, adapt, and make better decisions over time. If you're interested in the architecture or would like to explore the project further, you can find the source code here: GitHub: https://github.com/siddharthg-7/DevPilot-Ai- I'm always interested in feedback and discussions around persistent memory, runtime intelligence, and AI engineering. If you've explored similar ideas or approached these challenges differently, I'd love to hear your perspective.

I recently completed an exploratory data analysis project on the NHANES (National Health and Nutrition Examination Survey) dataset from Kaggle. It's a real-world health survey collected by the CDC covering body measurements, lifestyle habits, and demographic data from thousands of US adults. In this article I'll walk you through exactly what I did — from loading and cleaning the data all the way to running statistical tests — and share what I found along the way. The Dataset The dataset has 5,735 rows and 28 columns , but for this project I focused on 8 columns that were relevant to the questions I wanted to answer: Column Description smoking Has the person smoked at least 100 cigarettes? gender Male or Female age Age in years education Highest level of education weight Weight in kg height Height in cm bmi Body Mass Index Step 1 — Loading and Selecting Columns import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns db = pd . read_csv ( ' NHANES.csv ' ) data = db . loc [:, ( ' SEQN ' , ' SMQ020 ' , ' RIAGENDR ' , ' RIDAGEYR ' , ' DMDEDUC2 ' , ' BMXWT ' , ' BMXHT ' , ' BMXBMI ' )] data = data . rename ( columns = { ' SEQN ' : ' id ' , ' SMQ020 ' : ' smoking ' , ' RIAGENDR ' : ' gender ' , ' RIDAGEYR ' : ' age ' , ' DMDEDUC2 ' : ' education ' , ' BMXWT ' : ' weight ' , ' BMXHT ' : ' height ' , ' BMXBMI ' : ' bmi ' }) One thing worth knowing about NHANES: all the columns come in as numeric codes. 1 means Male, 2 means Female. 1 means the person smoked, 2 means they didn't. You have to map these to readable labels before doing any analysis, otherwise your charts are meaningless. Step 2 — Cleaning the Data Drop the ID column and remove nulls data . drop ( ' id ' , axis = 1 , inplace = True ) data . dropna ( inplace = True ) This brought us from 5,735 rows down to 5,406 — about 6% lost, which is acceptable. Remove outliers using the IQR method The IQR (Interquartile Range) method flags values that fall too far outside the middle 50% of

For the last 3 months I recorded every session I had with Claude Code. Not screenshots, not memory. Every prompt I typed and everything it did, saved to a small database I own. I did it because I kept losing my own work. I would finish a week, someone would ask what I shipped, and I genuinely could not remember. The work was real. It just lived in terminal scrollback I would never scroll through again. So I set up a chain of small agents to remember it for me. Every night, while I sleep, one agent reads that day's raw sessions and writes a single clear note: what I built, the decisions I made, what is still open. Plain language, the way I would write it in a journal, not a wall of logs. Once a week, a second agent reads all seven daily notes and updates a profile of me: the projects I am moving, the skills I have actually used, the things I learned. After a few months this turned into a more honest picture of my work than my resume. Then a third agent reads all of that and drafts posts for LinkedIn and X about what I actually worked on that week. Building in public, without me having to remember or sit down and write. The part I like most: none of it runs on my machine. It is all scheduled cloud routines. My laptop can be off. I wake up and the notes, the profile, and the draft posts are already waiting. I have started open-sourcing this as Pulse. The capture and the nightly daily-note agent are out now. You point it at your own database and your own notes repo, and it writes your day for you, in plain English, in files you own. The weekly profile agent and the post-writer are the pieces I am extracting next. It is early and rough in places. The honest caveat: the writing is only as good as the model behind it, and a quiet day still makes a quiet note. But after 3 months, I no longer guess what I did. I just open the vault. The graph at the top is 3 months of my own notes, each day linked to the projects it touched. Repo: https://github.com/muhammademanaftab/pulse