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Hacktoberfest Open-source AI Challenge l: Week 4

NitinShekhar27 — Sun, 01 Nov 2026 06:05:34 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine

🕯️ Velaria — A Ghost in the Machine

What I Built

I built Velaria, a privacy-focused remembrance application that helps people preserve meaningful memories of the people they care about.

The idea behind Velaria is simple: even when someone is no longer physically present, their stories, photographs, and voice can continue to live on.

Velaria allows users to:

Generate warm and meaningful remembrances using AI.
Save and revisit memories.
Attach photographs to memories.
Record and preserve voice notes.
Access saved memories without an internet connection.

I interpreted the Ghost in the Machine theme as the idea of preserving a person's presence through the memories they leave behind. Velaria brings together local AI, personal stories, photos, and voice recordings to create a space where those memories can remain accessible.

Demo

Project repository:

https://github.com/NitinShekhar27/velaria

I tested the main features of Velaria, including generating remembrances, saving memories, attaching photos, recording voice notes, and accessing the application offline.

Code

🔗 GitHub Repository:

https://github.com/NitinShekhar27/velaria

Velaria is an open-source project. The repository contains the application code and setup instructions.

How I Built It

I built Velaria using Python and integrated a locally running AI model through Ollama.

The application uses the llama3.2:3b model for remembrance generation. Instead of depending on a cloud-based AI API, Velaria uses local inference on the user's computer.

The main technologies used are:

Python — Application logic
Ollama — Running the AI model locally
Llama 3.2 (3B) — Local AI remembrance generation
Local storage — Saving memories and their attachments

I tested the application with sample memories and confirmed that the main features continued to work after disconnecting from the internet.

Why Does Open Innovation Matter?

Open innovation made it possible for me to build an AI-powered application without depending on a closed, cloud-based AI service.

Using an open-weight model through Ollama gave me the opportunity to experiment with local AI and understand how AI can be integrated into a real application.

For a project like Velaria, local AI is especially meaningful because memories can be personal. Keeping AI generation and saved content on the user's own computer helps support a more privacy-focused experience.

Open innovation also makes it easier for other developers to explore the project, learn from it, and contribute improvements.

The Spirit of It

Velaria was inspired by the idea that memories can keep a person's presence alive in our everyday lives.

A photograph can remind us of a moment. A voice recording can preserve the way someone spoke. A story can bring back a feeling that we thought we had forgotten.

With Velaria, I wanted to explore how local AI could help preserve these meaningful pieces of the past.

The goal is not to replace real memories or relationships, but to create a simple digital space where stories, photos, and voices can be kept and revisited.

Prize Categories

I am submitting Velaria for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine.

Ghosts in the Machine: Why Dead Code Haunts Developers and How Open-Source Tools Can Save Them

Tamiz Uddin — Sun, 01 Nov 2026 06:04:51 +0000

Originally published on tamiz.pro.

Every codebase has ghosts. They're not the kind that go bump in the night — they're the functions nobody calls, the branches nobody reaches, and the dependencies nobody imports. Dead code is the quietest form of technical debt: it compiles, it lints, it passes tests, and it does absolutely nothing. Left alone, it multiplies.

This article dissects the anatomy of dead code, quantifies the damage it silently inflicts on your build times, bundle size, and team morale, and then walks through a battle-tested arsenal of open-source tools — knip, depcheck, madge, ts-prune, and more — that can exorcise it from your project for good.

1. What Dead Code Actually Is
2. The Real Cost of Dead Code
3. How Ghosts Sneak In
4. The Open-Source Ghostbusting Arsenal
5. A Practical Ghostbusting Workflow
6. A Real-World Exorcism, Step by Step
7. When Dead Code Is (Sometimes) Fine
Frequently Asked Questions

1. What Dead Code Actually Is

"Dead code" is a catch-all term, but not all ghosts are the same. Understanding the taxonomy matters because each type requires a different detection strategy.

Unreachable code — statements that can never execute because of an unconditional return, throw, break, or an infinite loop before them.

function explode(): never {
  throw new Error("boom");
  console.log("I can never run"); // unreachable
}

Unused private code — functions, variables, classes, or imports that are declared but never referenced anywhere in the module.

function unusedHelper(): number {
  return 42;
}

export function publicApi(): number {
  return 1;
}

Orphaned public code — exported functions or components that nothing in the codebase imports. This is the sneakiest kind: it looks legitimate, passes type-checking, and ships in your bundle, but no runtime path ever touches it.

Redundant code — logic that has been superseded. A switch branch that can never be reached because an earlier branch handles the same case; an if condition that is always true after a refactor.

Stale feature-flag code — branches guarded by a feature flag that has been permanently enabled or disabled. The flag check remains, but one side of the conditional is now frozen in place.

2. The Real Cost of Dead Code

Dead code doesn't just sit there looking ugly. It compounds across every axis of software delivery.

Bundle size and load time

Bundlers like webpack and Rollup can tree-shake statically analyzable unused exports, but they can't remove everything. Side-effectful modules, dynamic imports, and code accessed via reflection all resist elimination. A single dead module that imports a 200 KB charting library can silently double your initial bundle.

Compile time and CI cost

Every dead file is a file TypeScript must parse, type-check, and emit. Every dead dependency is a package npm install must resolve, and a node module the bundler must walk. In a monorepo with hundreds of packages, a 5% dead-code ratio can add minutes to every CI run — multiplied across every contributor, every day.

Cognitive load and onboarding

Dead code is noise. When a new engineer reads a file, they must mentally execute every branch to determine what matters. A codebase where 15% of functions are never called forces developers to build a mental model of code that doesn't exist. This is the "ghost" in the title: code that feels alive but has no pulse.

Security and supply-chain surface

This is the most underrated cost. A dead import of a vulnerable package still gets installed, still gets scanned by your SCA tool, and still generates a CVE alert. You can't "patch" a dependency you don't use — you have to remove it, which means finding every dead import first. Dead code also hides attack surface: an unused Express route handler is still registered middleware.

Test maintenance

Code that is never called at runtime often has tests written for it anyway. Those tests are also dead. They run on every CI pass, slow down the suite, and give a false sense of coverage. Worse, they lock in behavior you may actually want to delete.

License compliance

Unused dependencies still count toward your license obligations. A dead GPL-licensed package in a proprietary product is a legal liability even if no line of your code imports it.

3. How Ghosts Sneak In

Dead code rarely arrives as a deliberate commit. It creeps in through a handful of well-worn paths.

Refactoring leftovers — you extract a function, delete the original call site, and forget to delete the original body. The most common origin.

Feature-flag removal — you ship a feature, remove the flag from your config, but leave the if (flag) wrapper and its now-dead else-branch behind.

Copy-paste and vendoring — you copy a utility file into your project, use one function, and keep the rest.

Framework scaffolding — create-react-app, next init, and similar generators ship starter components and assets you never touch.

Abandoned experiments — a spike branch merged by accident, a prototype that lost the argument but stayed in the tree.

Gradual API deprecation — you mark a function @deprecated, add a replacement, and plan to remove it in v2. The v2 never ships.

4. The Open-Source Ghostbusting Arsenal

The good news: the open-source ecosystem has matured a complete toolkit for this. Here's the arsenal, organized by detection strategy.

4.1 Compiler and linter flags (per-file static analysis)

The cheapest detection is the compiler itself. TypeScript ships two flags that catch the most common dead code at the module level:

// tsconfig.json
{
  "compilerOptions": {
    "noUnusedLocals": true,
    "noUnusedParameters": true
  }
}

noUnusedLocals flags local variables and private functions that are never read. noUnusedParameters flags function parameters never referenced. ESLint's @typescript-eslint/no-unused-vars rule does the same and is more configurable (you can allow _-prefixed args, for example).

These catch local ghosts but miss exported symbols, which is where the bigger problem lives.

4.2 `ts-prune` — find orphaned exports

ts-prune uses the TypeScript compiler API to walk your entire program and report every exported symbol that is never imported anywhere else.

npx ts-prune

Output looks like this:

src/utils/format.ts:3:2 - formatBytes
src/utils/format.ts:9:2 - formatCurrency
src/components/Button.tsx:1:2 - Button

Each line is an export you can safely delete — if you're not a library publishing that API.

4.3 `depcheck` — unused and missing dependencies

depcheck analyzes your package.json against your source to find packages you installed but never import, and packages you import but forgot to install.

npx depcheck

It supports a .depcheckrc file for ignoring intentional entries:

{
  "ignores": ["@types/*", "eslint", "prettier"],
  "ignorePatterns": ["dist", "build", "coverage"]
}

4.4 `knip` — the modern Swiss-army knife

knip is the most powerful open-source tool in this space. It combines dependency detection, unused export detection, unused file detection, and workspace awareness in one binary. It understands monorepos, workspaces, and entry-point conventions, and it's fast because it uses the TypeScript compiler API.

npx knip

Example output:

Unused files (2)
src/legacy/old-parser.ts
src/experiments/prototype.ts

Unused dependencies (3)
lodash
moment
axios

Unused devDependencies (1)
@types/node

Unused exports (5)
src/utils/format.ts:3:2 formatBytes
src/utils/format.ts:9:2 formatCurrency
src/components/Button.tsx:1:2 Button
src/api/client.ts:12:2 createClient
src/hooks/useOldHook.ts:2:2 useOldHook

knip is configured via knip.json:

{
  "$schema": "https://unpkg.com/knip@5/schema.json",
  "entry": ["src/index.ts", "src/main.tsx"],
  "project": ["src/**/*.ts", "src/**/*.tsx"],
  "ignore": ["**/*.test.ts", "**/*.spec.ts"],
  "ignoreDependencies": ["@types/node", "eslint", "prettier"]
}

4.5 `madge` — orphaned modules and circular dependencies

madge builds your module dependency graph. It can detect circular dependencies (a common source of subtle bugs) and orphaned modules that nothing imports.

npx madge --circular src/

✖ Found 2 circular dependencies!

1) src/index.ts -> src/services/auth.ts -> src/utils/token.ts -> src/index.ts
2) src/components/App.tsx -> src/components/Header.tsx -> src/components/App.tsx

Circular dependencies are a cousin of dead code: they make tree-shaking harder and can cause undefined initialization bugs.

4.6 Coverage tools — find code tests never execute

Coverage tools like c8, nyc, and Vitest's built-in coverage provider identify lines and branches that your test suite never reaches. This is the most behavioral form of dead-code detection — it finds code that is technically reachable but has no tests, which is often a proxy for code nobody cares about.

npx vitest run --coverage

Set a threshold in vitest.config.ts to enforce it in CI:

import { defineConfig } from "vitest/config";

export default defineConfig({
  test: {
    coverage: {
      provider: "v8",
      lines: 90,
      functions: 90,
      branches: 85,
      statements: 90,
    },
  },
});

4.7 Bundler tree-shaking and minification

Your bundler is the last line of defense. Rollup, esbuild, and webpack 5+ can drop unused exports from ES modules, and minifiers like terser and swc can eliminate unreachable branches.

// rollup.config.js
export default {
  input: "src/index.js",
  output: { file: "dist/bundle.js", format: "esm" },
  treeshake: {
    moduleSideEffects: false,
    propertyReadSideEffects: false,
  },
};

moduleSideEffects: false tells Rollup that importing a module has no side effects, so it can drop the entire module if none of its exports are used. This is powerful but requires discipline: any module with real side effects (polyfills, CSS imports, global registrations) must be marked as side-effectful or the bundler will incorrectly remove it.

5. A Practical Ghostbusting Workflow

Here's a workflow you can adopt in an afternoon. The goal: catch dead code at every stage — locally, in review, and in CI.

Step 1 — Enable compiler flags. Turn on noUnusedLocals and noUnusedParameters in tsconfig.json. This catches the low-hanging fruit immediately.

Step 2 — Add knip as your primary detector. It covers the widest surface: unused files, exports, dependencies, and workspaces. Add a knip.json config, then run it locally and fix the top-level findings.

Step 3 — Add depcheck as a second opinion. knip and depcheck overlap but each has blind spots. depcheck is better at detecting missing dependencies; knip is better at detecting unused exports and files. Run both.

Step 4 — Run madge --circular. Break the cycles. Circular dependencies are often the reason a module can't be tree-shaken, so fixing them unlocks dead-code removal elsewhere.

Step 5 — Set coverage thresholds. Force every new line of code to be exercised by a test. This prevents new dead code from sneaking in via untested paths.

Step 6 — Wire it into CI. Fail the build on regressions. Add a lint:dead script and a CI step.

// package.json
{
  "scripts": {
    "lint:dead": "knip --production && depcheck",
    "test:coverage": "vitest run --coverage",
    "check:cycles": "madge --circular src/"
  }
}

# .github/workflows/ci.yml
- name: Check for dead code
  run: npm run lint:dead

- name: Check for circular dependencies
  run: npm run check:cycles

6. A Real-World Exorcism, Step by Step

Let's trace a real cleanup on a small TypeScript project. Here's the file tree:

src/
  index.ts
  utils/
    format.ts
    legacy.ts
  components/
    Button.tsx
    Modal.tsx
  api/
    client.ts

Run knip:

npx knip

Output:

Unused files (1)
src/experiments/prototype.ts

Unused dependencies (2)
lodash
moment

Unused exports (3)
src/utils/format.ts:3:2 formatBytes
src/utils/format.ts:9:2 formatCurrency
src/components/Modal.tsx:1:2 Modal

Now run depcheck:

npx depcheck

Output:

Unused dependencies
* lodash
* moment

Missing dependencies
* zod

depcheck reveals two things knip missed: zod is imported somewhere but missing from package.json, and both tools agree lodash and moment are unused.

Run madge --circular:

npx madge --circular src/

✖ Found 1 circular dependency!
src/index.ts -> src/api/client.ts -> src/index.ts

Now the fixes:

Delete src/experiments/prototype.ts — it's a dead file.
npm uninstall lodash moment — dead dependencies.
npm install zod — missing dependency.
Delete formatBytes and formatCurrency from format.ts — orphaned exports.
Delete Modal.tsx — orphaned component.
Break the cycle between index.ts and api/client.ts — usually by extracting the shared type or moving the import.

Re-run all three tools:

npx knip && npx depcheck && npx madge --circular src/

Clean output. Your bundle is smaller, your CI is faster, and your next developer won't have to reverse-engineer a ghost.

7. When Dead Code Is (Sometimes) Fine

Not all dead code should be removed. Know the exceptions:

Public library APIs. If you publish an npm package, your exported functions are your contract. Removing them is a breaking change. Dead code here is a semver problem, not a hygiene problem. Use knip with an entry/ignore config for your public API, or mark the exports as intentional.

Feature flags you plan to reuse. A flag that's toggled off in production but will return is not dead — it's dormant. Keep it, but track it.

Generated code. Build artifacts, generated API clients, and schema types are regenerated. Don't hand-edit them, and configure your tools to ignore them.

Defensive code for impossible states. A branch that handles a theoretically impossible input is a safety net. Removing it can turn a graceful failure into a crash. If you keep it, add a comment explaining why.

Dead code as documentation. Rare, but occasionally a function is kept solely to illustrate an API pattern. This is a judgment call — and it should be a deliberate one, not an accident.

Frequently Asked Questions

Will removing dead code break my app?

No — if the code is genuinely dead. The risk is misidentification: a tool might flag an export that's actually used by a dynamic import(), a test file, or a plugin system. Mitigate this by running your full test suite after each removal, using coverage thresholds to confirm nothing regressed, and reviewing knip's findings before deleting. For libraries, treat exported API as live code regardless of internal usage.

What's the difference between knip and depcheck?

depcheck focuses on package.json dependencies — it tells you which packages you installed but never import, and which you import but never installed. knip is broader: it detects unused files, unused exports, unused types, unused dependencies, and works across monorepos and workspaces. knip is generally the better primary tool; depcheck is a useful second opinion for dependency-specific blind spots.

Should I remove dead code in a library?

Only if you're ready for a major version bump. In a library, an exported symbol is part of the public API even if nothing in your repo uses it. Remove it in the next major release, document the removal in a changelog, and use knip's ignore config to keep your public API out of the dead-code report.

Want more deep dives on developer tooling and codebase hygiene? Explore Tamiz's Insights or the tamiz.pro engineering blog.

Hands-On: Banishing Ghosts from a Real Codebase

Theory is great, but nothing beats the visceral satisfaction of watching dead code get deleted. Let's walk through a practical, end-to-end cleanup using the tools we've discussed. We'll use a realistic, if slightly contrived, TypeScript project to demonstrate the workflow.

The Haunted Project Setup

Imagine a small utility library called string-utils. It's been around for a few years and has accumulated some cruft. Here's the (simplified) file structure:

string-utils/
├── package.json
├── tsconfig.json
├── src/
│   ├── index.ts
│   ├── format.ts
│   ├── legacy/
│   │   ├── old-parser.ts
│   │   └── deprecated-helpers.ts
│   └── utils/
│       ├── is-email.ts
│       └── random.ts
└── tests/
    ├── format.test.ts
    └── is-email.test.ts

The src/index.ts is our public entry point:

// src/index.ts
export { formatDate, formatCurrency } from './format';
export { isEmail } from './utils/is-email';

We have a legacy folder that hasn't been touched in ages. Let's see what's inside.

// src/legacy/old-parser.ts
// This was used by a previous version of the app but is now obsolete.
export function parseLegacyString(input: string): string[] {
  // ... 50 lines of convoluted regex logic
  return input.split(',');
}

// src/legacy/deprecated-helpers.ts
export function toUpperCaseSafe(str: string): string {
  return str ? str.toUpperCase() : '';
}

export function toLowerCaseSafe(str: string): string {
  return str ? str.toLowerCase() : '';
}

And our utils folder:

// src/utils/is-email.ts
export function isEmail(value: string): boolean {
  const emailRegex = /^[^\s@]+@[^\s@]+\.[^\s@]+$/;
  return emailRegex.test(value);
}

// src/utils/random.ts
// A utility that was used once in a demo and then forgotten.
export function getRandomString(length: number): string {
  const chars = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789';
  let result = '';
  for (let i = 0; i < length; i++) {
    result += chars.charAt(Math.floor(Math.random() * chars.length));
  }
  return result;
}

Finally, src/format.ts:

// src/format.ts
export function formatDate(date: Date): string {
  return date.toISOString().split('T')[0];
}

export function formatCurrency(amount: number, currency: string): string {
  return new Intl.NumberFormat('en-US', {
    style: 'currency',
    currency: currency,
  }).format(amount);
}

// This function is used internally but never exported from index.ts.
// It's still "alive" because it's used within this module.
function padToTwoDigits(num: number): string {
  return num.toString().padStart(2, '0');
}

The Hunt Begins: Running `knip`

Let's run knip on this project. We'll install it first:

npm install -D knip
npx knip

The output will look something like this:

Unused files (2)
src/legacy/old-parser.ts
src/legacy/deprecated-helpers.ts

Unused dependencies (0)
Unused devDependencies (0)
Unlisted dependencies (0)

Unused exports (3)
getRandomString (used in 0 files)
toUpperCaseSafe (used in 0 files)
toLowerCaseSafe (used in 0 files)

Excellent! knip has identified the two files in the legacy folder as completely unused, and it's flagged three exports as unused. But wait—it didn't flag padToTwoDigits. Why? Because knip is smart enough to detect that it's used within format.ts (even though it's not exported). It's not dead code; it's just private.

The Vulture's Perspective

Now, let's run vulture on the same project to see if it catches anything different:

pip install vulture
vulture src/ --min-confidence 80

Vulture's output:

src/legacy/old-parser.ts:1: unused function 'parseLegacyString' (80% confidence)
src/legacy/deprecated-helpers.ts:1: unused function 'toUpperCaseSafe' (80% confidence)
src/legacy/deprecated-helpers.ts:1: unused function 'toLowerCaseSafe' (80% confidence)
src/utils/random.ts:1: unused function 'getRandomString' (80% confidence)

Vulture agrees on the obvious dead code, but it also flags parseLegacyString, which knip didn't. Why? Because knip treats files as "unused" if they aren't imported anywhere, while vulture analyzes the code within files. Since old-parser.ts is never imported, both tools consider it dead, but knip groups it as an unused file, while vulture looks at the function level.

The Exorcism: Removing the Ghosts

Now that we've identified the culprits, we can confidently delete them. Let's remove the entire legacy folder and the random.ts file:

rm -rf src/legacy
rm src/utils/random.ts

We also need to update src/index.ts if it exported anything from those files (it didn't, but it's good practice to check).

After removal, let's re-run knip to confirm the codebase is clean:

npx knip

Output:

Unused files (0)
Unused dependencies (0)
Unused devDependencies (0)
Unlisted dependencies (0)

Unused exports (0)

Clean! The ghosts are gone, and the codebase is leaner. But we're not done yet. Let's think about the why.

Why Did This Happen? A Post-Mortem

The dead code in our example didn't appear overnight. It was a gradual accumulation. Here's the likely timeline:

The Legacy Parser: This was written for a v1 of the application. When v2 introduced a new parsing strategy, the old parser was left in place "just in case." That "just in case" never came.
The Deprecated Helpers: These were utility functions that were used in a few places. Over time, refactoring replaced them with more robust alternatives (like the native toUpperCase method), but the old helpers were never removed.
The Random String: This was added for a demo feature that was later scrapped. The function remained, unused, in the codebase.

This is the classic pattern of dead code accumulation. It's rarely a single act of negligence; it's a thousand small decisions to "leave it for now" that compound over time.

Advanced Patterns: When Tools Need Help

Sometimes, even the best tools need a little human guidance. Let's explore a few scenarios where you might need to configure your dead-code detectors.

Scenario 1: Dynamic Imports

Consider a plugin architecture where modules are loaded dynamically:

// src/plugins/index.ts
import fs from 'fs';
import path from 'path';

const pluginFiles = fs.readdirSync(__dirname).filter((file) => file.endsWith('.js'));

export function loadPlugins() {
  return pluginFiles.map((file) => {
    // Dynamic import - tools can't statically analyze this!
    return import(path.join(__dirname, file));
  });
}

knip and vulture will flag all your plugin files as unused because they can't trace the dynamic import. You need to configure them.

For knip, you can use the include or ignore options in knip.json:

{
  "include": ["src/**/*.ts"],
  "ignore": ["src/plugins/**/*.ts"]
}

Alternatively, you can use the entry configuration to tell knip that the plugin files are entry points:

{
  "entry": ["src/index.ts", "src/plugins/**/*.ts"]
}

For vulture, you can use --ignore flags:

vulture src/ --ignore "src/plugins/*"

Scenario 2: Public API Exports

If you're building a library, your public API is the contract with your users. Tools might flag exports as unused if they're not used within the project, but they're part of the public interface. This is where configuration becomes crucial.

For knip, you can define which files are entry points (the public API):

{
  "entry": ["src/index.ts"]
}

This tells knip that src/index.ts is the entry point, and all exports from it are considered used. However, if you have a file like src/utils/helpers.ts that exports functions used only in tests, you might want to configure knip to ignore test files:

{
  "ignore": ["tests/**/*.ts"]
}

For vulture, you can use the --ignore-decorators or --exclude options, but the most straightforward way is to add a vulture_whitelist.py file:

# vulture_whitelist.py
# This file is used to whitelist functions that are part of the public API.
from src.utils.helpers import publicFunction

publicFunction  # noqa

This tells vulture that publicFunction is used (by the whitelist itself), so it won't be flagged.

Scenario 3: Conditional Exports and Environment Variables

Sometimes, code is conditionally exported based on environment variables:

// src/index.ts
export * from './utils/is-email';

if (process.env.NODE_ENV === 'development') {
  export * from './utils/dev-tools';
}

Static analysis tools will see the export * from './utils/dev-tools' and might not flag it, but if NODE_ENV is never 'development' in production, that code is effectively dead in production. Tools like knip can't easily handle this, so you'd need to either:

Use a build-time constant that the tool can understand.
Manually ignore the file.
Use a tool like eslint-plugin-no-restricted-imports to prevent accidental imports.

The Psychological Toll: Why We Don't Delete

We've covered the technical side, but let's address the human side. Why do developers feel so much resistance to deleting code?

The "What If" Factor: We're conditioned to think that code might be useful someday. This is a cognitive bias called loss aversion—we feel the pain of losing something more than the pleasure of gaining something. But dead code is not an asset; it's a liability.
Fear of Breaking Things: We worry that deleting code will break something we can't see. This is where automated tests become your safety net. If you have good test coverage, you can delete with confidence.
The "It's Not My Code" Problem: If you didn't write it, you might feel uncomfortable deleting it. But code ownership is a myth. Once code is in the repository, it's the team's responsibility.
Lack of Time: We're always behind schedule. Deleting code feels like a luxury, not a necessity. But dead code slows down development—it increases cognitive load, makes code harder to navigate, and can even cause bugs (like when a developer uses a deprecated function thinking it's the current one).

The Business Case for Clean Code

Let's quantify the cost of dead code. A study by Microsoft Research found that developers spend about 50% of their time on "technical debt"—code that isn't adding new value. Dead code is a significant contributor to this.

Consider the following:

Cognitive Load: Every time you open a file, you have to understand what it does. If 20% of the code is dead, you're wasting 20% of your reading time.
Build Times: Dead code still needs to be compiled or transpiled. Removing it can speed up your build.
Bundle Size: For frontend applications, dead code increases bundle size, leading to slower page loads and higher bandwidth costs.
Security: Dead code can contain vulnerabilities. Attackers can exploit code that you forgot you had.

In our example, the legacy folder contained regex logic that might have had ReDoS (Regular Expression Denial of Service) vulnerabilities. By deleting it, we've reduced our attack surface.

The Future: AI-Assisted Dead Code Detection

We're on the cusp of a new era. Large Language Models (LLMs) like the one you're reading this on are starting to be used for code analysis. Tools like CodeQL and Semgrep are already using static analysis to find security vulnerabilities, and they're getting better at finding dead code.

Imagine a tool that can:

Analyze your entire codebase and understand the intent of every function.
Identify code that is not just unused, but conceptually dead—code that doesn't contribute to the application's purpose.
Suggest refactorings that consolidate similar functions.

This is still a research area, but the direction is clear. The tools we've discussed today are the foundation; the future will be smarter, faster, and more integrated.

Conclusion: Embrace the Delete

Dead code is not a technical problem; it's a cultural one. We need to shift our mindset from "code is an asset" to "code is a liability." Every line of code is a line that needs to be understood, tested, and maintained.

The tools we've explored—knip, vulture, ts-prune, eslint-plugin-unused-imports, and jscpd—are not just utilities; they are enablers of a healthier codebase. They give us the confidence to delete, to refactor, and to move forward without the ghosts of the past weighing us down.

So, the next time you see a function that hasn't been called in months, or a file that hasn't been imported in years, don't hesitate. Run your dead-code detector, verify the results, and hit delete. Your future self—and your teammates—will thank you.

This deep dive was brought to you by the team at Tamiz's Insights. For more on developer tooling, codebase hygiene, and the art of maintaining large-scale software, be sure to check out our engineering blog.

Kubernetes priority classes can preempt security controls

yutianle — Sun, 01 Nov 2026 06:04:11 +0000

Kubernetes priority classes can preempt security controls

Kubernetes priority controls scheduling order, not trust. A PriorityClass maps a name to an integer, and a pod with a higher value may cause lower-priority pods to be evicted when the cluster needs capacity. The feature is useful for latency-sensitive services and critical jobs. It can also remove the pod that performs logging, policy enforcement, or incident response.

The Kubernetes documentation warns that users who can create high-priority pods can disrupt other workloads in a cluster where not all users are trusted. That warning applies beyond ordinary applications. An admission controller, security agent, or monitoring collector with a modest priority can be displaced by a workload with a higher one. A PodDisruptionBudget does not make the control absolute. Kubernetes supports PDBs during preemption, but the scheduler treats them as best effort and may still preempt pods when no acceptable victim set is found.

Priority is a cluster-scoped decision

PriorityClass is a cluster-scoped API object. Higher numbers mean higher priority. A default class can apply when a workload omits priorityClassName, while a pod that explicitly names a class receives that value. The scheduler uses priority when choosing victims, and kubelet node-pressure eviction also considers pod priority alongside resource usage and QoS.

Those interactions make a simple policy review insufficient. A namespace may look constrained while a workload can select a dangerous cluster-wide class. A service account may create jobs that inherit a high-priority template. A namespace quota can limit the number of pods, but it does not automatically prevent one pod from using a preempting class.

The safer pattern is to limit which identities may create or use privileged classes, make production classes explicit, and keep security infrastructure on nodes or workloads whose failure is separately budgeted. Check whether the control plane can continue collecting evidence when a high-priority workload triggers repeated evictions.

Test the actual failure path

Create a non-production cluster or namespace with the same admission policies and node pool configuration. Record the priority values of security agents, DNS, ingress, logging, and application tiers. Submit a pod with preemptionPolicy: Never first and confirm that it waits without evicting anything. Then, only in the test environment, exercise a preempting class and observe events, PDB behavior, restart policy, and loss of telemetry.

Review ResourceQuota, RBAC, admission policy, and the default PriorityClass together. A quota that blocks pods is not enough if it permits a single pod with a cluster-wide high priority. Use kubectl get priorityclass and inspect workload templates for explicit values. Add an alert for preemption events and for a drop in the expected replica count of security services.

Priority classes should express availability requirements with a clear owner. A high number is not a substitute for High Availability, scheduling constraints, or protected network paths. The team that can raise a workload's priority can also decide which lower-priority controls disappear first.

References

Kurumsal ulaşım operasyonunu dijitalleştirmek: nereden başlanır

Ali şahin BALCIOĞLU — Sun, 01 Nov 2026 06:03:43 +0000

Sorun teknik değil, operasyonel

Saha ile çalışan çoğu firmada ulaşım operasyonu hâlâ aynı şekilde yürüyor: bir WhatsApp grubu, birkaç excel dosyası ve telefonla teyit edilen güzergâhlar. Bu düzen küçük ölçekte işliyor gibi görünür, ama personel sayısı ve araç sayısı büyüdükçe çatlaklar aynı üç yerden açılıyor: kimin nerede olduğunu bilmemek, acente hakedişlerini elle hesaplamak ve bir ihlal yaşandığında geriye dönük kanıt bulamamak. Dijitalleşme kararı genelde bu üç kör noktadan biri patladığında alınıyor — bir sefer gecikmesi, bir hakediş itirazı ya da bir denetimde kayıt gösterememek.

İlk soru: neyi göreceksiniz?

Bir kurumsal ulaşım operasyonunu yazılıma taşırken ilk karar görünürlük kapsamını belirlemek. Sahadaki bir müşteri için kurduğumuz personel servis yönetim sisteminde bu kapsamı dört ayrı uygulamaya böldük: yönetim paneli, taşıma acentesi paneli, şoför mobil uygulaması ve personel mobil uygulaması. Bunun nedeni basit — her rolün görmesi gereken bilgi farklı, tek bir ekranda herkese her şeyi göstermeye çalışmak kullanılmayan bir arayüz üretiyor. Şoförün görmesi gereken güzergâh ve durak bilgisi; acente yöneticisinin görmesi gereken filo ve hakediş; yönetimin görmesi gereken ise tüm operasyonun özeti.

Konum takibi bir özellik değil, temel

Canlı konum takibini sona bırakılacak bir ek özellik olarak görmüyoruz. Şoför konumu ve sefer durumunu gerçek zamanlı akıtmak — bizim durumumuzda Socket.io ve PostGIS coğrafi sorgularıyla — sistemin geri kalanının üzerine oturduğu zemin. Hakediş hesaplaması, ihlal kaydı, sefer tamamlama onayı; bunların hepsi "bu araç gerçekten o güzergâhta, o saatte oradaydı" bilgisine dayanıyor. Bu bilgi yoksa sistem bir dijital excel'den ibaret kalıyor; var olan sorunu dijital bir kılıfa sarmış olursunuz, çözmüş olmazsınız.

Hakediş: elle hesaplanan her şey er geç tartışmalı hale gelir

Çok acenteli bir yapıda — personel servis sisteminde 13 farklı taşıma acentesinin tek sistem üzerinde, verileri birbirinden izole şekilde çalıştığı bir kurulumdan bahsediyoruz — hakediş hesaplaması en hassas nokta. Elle tutulan excel tabloları ay sonunda kimin ne kadar sefer yaptığı, hangi ihlalin kesintiye yol açtığı konusunda tartışma yaratıyor. Bunu otomatikleştirdiğimizde ortaya çıkan şey sadece zaman kazancı değil; itiraz konusu azalıyor çünkü hesaplama mantığı sistemde sabit ve herkes aynı veriye bakıyor.

İhlal yönetimi: iz bırakmayan kayıt, kayıt değildir

Sefer ihlallerinin kayıt altına alınması ayrı bir başlık hak ediyor. Bir ihlalin ne zaman, hangi sefer için, hangi gerekçeyle işlendiği iz bırakmıyorsa, bir anlaşmazlık anında elinizde hiçbir şey kalmıyor. Buradan çıkardığımız ders şu: denetlenebilirlik sonradan eklenecek bir katman değil, sistemin tasarımına en baştan girmesi gereken bir gereklilik. Rol bazlı yetkilendirme (süper admin, admin, acente yöneticisi, şoför, personel), OTP'li giriş, JWT rotasyonu ve loglarda PII maskeleme gibi güvenlik kararları da aynı mantığın parçası: kim, neyi, ne zaman yaptı sorusuna her zaman cevap verebilmek.

Ölçek büyüdükçe öncelikler değişiyor

Daha büyük ölçekli bir kurumsal ulaşım operasyonunda çalıştığımız bir projede gördüğümüz şey şuydu: küçük ölçekte "görünürlük yeterli mi" sorusu yetiyorken, ölçek büyüdüğünde web ön yüz, backend ve native mobil uygulamanın birlikte, tek bir operasyonel gerçeklik üzerinden çalışması gerekiyor. Burada mimari kararların erken alınması önem kazanıyor; sonradan "aslında web ve mobil farklı veriler gösteriyormuş" demek, güven kaybına yol açan bir hata.

Nereden başlamalı

Bize soran firmalara verdiğimiz cevap genelde aynı: önce hangi kör noktanın en çok acı verdiğini bulun.

Sorun "kimin nerede olduğunu bilmiyoruz" ise, önce konum ve durum takibini kurun.
Sorun "hakediş hesapları her ay tartışma yaratıyor" ise, önce otomatik hakediş motorunu kurun.
Sorun "bir ihlal olduğunda elimizde kanıt kalmıyor" ise, önce kayıt ve denetim katmanını kurun.

Her şeyi aynı anda çözmeye çalışmak, hiçbir şeyi zamanında bitirmemekle sonuçlanıyor. Bütünleşik sistem elbette hedef, ama oraya giden yol kademeli olmalı — önce en acil kör noktayı kapatıp, ardından diğer modülleri üzerine inşa etmek.

Önümüzdeki adım: ulaşımın ötesi

Şu an üzerinde çalıştığımız bir seyahat ve rezervasyon yönetimi projesi, bu düşünceyi bir adım öteye taşıyor: kurumsal ulaşımda kurduğumuz görünürlük ve denetlenebilirlik mantığının, seyahat planlaması ve rezervasyon süreçlerine de uygulanabileceğini düşünüyoruz. Bu proje henüz canlı değil, tamamlanmış bir ürün olarak sunmuyoruz — ama bizim için gösterdiği şey şu: manuel, dağınık operasyonları yazılıma taşıma ihtiyacı tek bir sektörle sınırlı değil. WhatsApp grubu ve excel dosyasıyla yönetilen her saha operasyonu aynı üç kör noktadan muzdarip: görünürlük, hakediş, denetim.

Sonuç

Kurumsal ulaşım operasyonunu dijitalleştirmek bir yazılım satın alma kararı değil, bir önceliklendirme kararı. Doğru soru "hangi yazılımı alalım" değil, "operasyonumuzun hangi noktası kör ve bu körlük bize ne kadara mal oluyor" sorusu. Bu soruyu doğru cevapladığınızda geri kalanı — mimari, teknoloji seçimi, uygulama sırası — çok daha net hale geliyor.

What we learned building a personal AI assistant that lives in WhatsApp

Kubi Team — Sun, 01 Nov 2026 06:03:13 +0000

Most people we wanted to help were never going to install another AI app. Parents, busy friends, people running a small business: they already live in WhatsApp. So we built Kubi, a personal AI assistant that lives there. You text it, send it a photo or a voice note, and it does the task.

This post is about the design decisions that turned out to matter more than the model choice.

1. The chat is the whole interface

There is no dashboard to fall back on, so everything has to work as a conversation:

Reminders are set by just saying them ("remind me Thursday at 9 to call the bank"). A reminder can also be a phone call, which works well as a wake-up call.
A photo of a letter or a bill comes back as a plain-language explanation: who sent it, what they want, by when. Then it offers to remind you before the deadline. Official letters are confusing in every country, so this is the one people usually try first.
Voice notes in, and spoken replies out when you ask for them.
Web tasks: searching flights, comparing prices, signing in to a site on your behalf.

The hard part was not any single feature. It was keeping replies short enough to read on a phone, and asking one clear question when something is ambiguous instead of guessing.

2. Use the official WhatsApp Cloud API, and design for its rules

We use Meta's official Cloud API, not an unofficial bridge on a phone. That costs some flexibility, but it is the supported path, and unofficial bridges risk getting the number banned. It comes with rules you have to design around from day one:

You can reply freely inside a 24-hour window after the user's last message. Outside that window, a business can only send pre-approved templates. Anything proactive (a reminder, a follow-up) has to respect that.
Media (photos, voice notes) arrives as an id you fetch separately, so the pipeline has to download, transcribe or read it, then answer, all without leaving the user staring at a silent chat.

3. Every user gets their own isolated agent

Each user's agent runs in its own isolated environment (its own pod), with its own files and memory. One user's data is never in another user's context. This made some things harder (cold starts, scheduling), but it made the trust story simple to explain: your assistant is yours.

4. The model never sees your passwords

If an assistant can sign in to sites for you, the obvious risk is the password ending up in a prompt or a log. Our answer: credentials live in an encrypted vault, and a proxy fills them into the outgoing request. The agent can use a login without the secret ever entering the model's context.

5. Anything sensitive waits for an explicit OK

Payments, deletions and changes to your accounts do not happen on the model's judgment. The agent stops and asks in the chat, and only continues after you approve. Routine reading and searching does not need approval; otherwise the assistant becomes a stream of "are you sure?" messages and nobody uses it.

6. Onboarding is also a conversation

New people don't fill a form. They message Maya, our onboarding assistant, on WhatsApp. She explains what Kubi does and puts them on the early-access list. We let people in in small batches so we can actually read the feedback.

What we'd tell anyone building on WhatsApp

Start from the platform's rules (24-hour window, templates), not from the model.
Isolation per user is worth the operational cost.
Keep secrets out of the model context entirely; don't rely on "the prompt says don't print it".
Approval gates only for things that are actually sensitive.

Kubi works in English and Hebrew today, and it's free during early access. If you want to try it, message Maya on WhatsApp to request access: https://app.heykubi.ai/wa/3d618673 (or read more at https://heykubi.ai).

We'd love to hear from people building similar things: what's the first task you'd hand to an assistant like this, and what would make you not trust it?

The Kubi team

Why Generic Backends Fail AI Agents in 2026

ForgeWorkflows — Sun, 01 Nov 2026 06:02:54 +0000

The Infrastructure Gap Nobody Warned You About

In 2026, according to McKinsey's State of AI 2024 report, 72% of organizations use AI in at least one business function, up from 50% in prior years. Most of those deployments share a dirty secret: the infrastructure underneath them was never designed for autonomous, multi-step reasoning pipelines. Builders grabbed Firebase, Supabase, or a custom REST layer, bolted an LLM on top, and shipped. Then the state bugs started.

The problem isn't that those platforms are bad. It's that they were designed for request-response applications where a user clicks a button and expects a result within 200 milliseconds. Autonomous pipelines don't work that way. A research module hands off to a scoring module, which conditionally triggers a writing module, which may loop back to research if confidence is low. That's not a CRUD operation. It's a directed graph with mutable shared state, and generic BaaS tools treat it like a to-do list.

How Traditional BaaS Handles State (and Where It Breaks)

Firebase and Supabase are genuinely excellent for what they were built to do. Real-time listeners, row-level security, auth flows, file storage: these are solved problems on both platforms. When you're building a SaaS dashboard or a mobile app, you reach for one of them and you're done in a day.

Autonomous pipelines introduce three failure modes those platforms weren't designed to handle.

Concurrent state mutation. When two modules write to the same record simultaneously, generic databases rely on the application layer to manage conflicts. In a synchronous web app, that's manageable because writes are user-triggered and sequential. In a multi-module pipeline running parallel branches, you get race conditions. We hit this building our first Autonomous SDR: the research module and the scoring module both tried to update the lead record at the same time, and the scorer occasionally overwrote research output it hadn't read yet. The fix required custom locking logic that had nothing to do with the actual business problem we were solving.

Handoff contracts. Generic BaaS platforms store data; they don't enforce the shape of data passing between pipeline stages. When I built that first three-module architecture, research, scoring, and writing all reported to a single orchestrator, it worked fine on five leads. At fifty, the scorer sat idle waiting on research output that had nothing to do with scoring. Splitting into discrete modules with explicit handoff schemas between them cut processing time and made each stage independently testable. That's why every build we ship now uses explicit inter-module schemas. Implicit data passing doesn't hold up when you add load.

Resumability. A web request that fails gets retried from the start. A pipeline that fails halfway through a 40-step reasoning chain cannot afford to restart from step one. Generic backends have no native concept of a checkpoint. You either build your own resumption logic or you lose the work. For short pipelines, this is annoying. For long-running autonomous tasks, it's a production incident.

What Purpose-Built Agent Infrastructure Actually Changes

Statebase takes a different starting position. Rather than adapting a general-purpose database to fit pipeline needs, it treats the pipeline as the primary abstraction. State is attached to a run, not a record. Modules read from and write to a run context that persists across steps, survives failures, and enforces the schema of each handoff.

That's a meaningful architectural difference. It means the infrastructure handles the coordination problem, and the module code handles the reasoning problem. Those two concerns stay separate.

For solo founders and small teams, the practical effect is that you stop writing infrastructure glue. You don't implement a locking system. You don't build a checkpoint table. You don't write a retry coordinator. You define your pipeline stages, declare the data shape each stage expects and produces, and the platform manages the rest.

This matters because the alternative is weeks of backend work before you've written a single line of reasoning logic. We've seen builders spend more time on state management plumbing than on the actual intelligence layer. That's a resource allocation problem, and it's especially acute for the indie developer or two-person startup that can't hire a dedicated backend engineer.

It's worth being direct about the tradeoff, though. Purpose-built platforms like Statebase are narrower by design. If your pipeline needs to serve as a general-purpose API, handle complex relational queries, or integrate with a legacy auth system, you'll hit the edges of what a specialized tool can do. The narrowness that makes it fast to start is the same narrowness that creates friction when requirements drift toward general application infrastructure. Statebase is the right tool for pipelines. It is not a replacement for Supabase when you're building a multi-tenant SaaS product.

Firebase vs. Statebase: A Practical Decision Framework

The comparison isn't really Firebase versus Statebase. It's "what is the primary abstraction your application needs?"

If your application is user-facing, request-driven, and needs auth, storage, and real-time sync, Firebase or Supabase is the correct choice. Both platforms have years of production hardening, large communities, and extensive documentation. You will not outgrow their capabilities for standard application patterns.

If your application is a pipeline, a reasoning chain, or an autonomous process that runs without direct user interaction at each step, the calculus shifts. The features you need most are run persistence, stage-level state isolation, handoff validation, and failure recovery. Generic BaaS platforms provide none of these natively. You build them yourself, or you use a platform that provides them out of the box.

Three questions help clarify the decision:

Does your pipeline have stages that run in sequence, with each stage depending on the output of the previous one? If yes, you need run-scoped state, not record-scoped state.
Do any stages run in parallel and write to shared data? If yes, you need coordination primitives that generic databases don't provide without custom code.
Can your pipeline restart from the beginning if it fails at step 30? If no, you need native checkpointing.

If you answered yes to any of those, a purpose-built platform will save you weeks of infrastructure work. If you answered no to all three, you probably don't need Statebase, and you should use whatever BaaS you already know.

There's a middle path worth naming: n8n, which we use extensively for orchestration, handles some of this coordination at the workflow layer. Execution history, retry logic, and node-level error handling are built in. For pipelines that live entirely inside n8n, you may not need a separate state management layer at all. The gap Statebase fills is most acute when your pipeline logic lives outside a workflow tool, in custom code or a framework like LangGraph, where you're responsible for your own state.

If you're evaluating how to structure multi-module pipelines more broadly, our post on TypeScript agents: library vs. raw APIs covers the tradeoffs between framework-managed state and hand-rolled coordination. And if you're concerned about what happens when autonomous pipelines fail silently, our piece on AI incident oversight blind spots is directly relevant.

The broader catalog of automation blueprints we've built lives at /blueprints, and each one reflects the same principle: infrastructure decisions made upfront determine how much of your time goes to the actual problem versus the plumbing around it.

What We'd Do Differently

We'd define handoff schemas before writing any module logic. Every time we've skipped this step, we've paid for it later. The schema is the contract. Write it first, even if it feels premature. When a module fails, you want to know immediately whether the failure is in the reasoning or in the data shape, and you can only do that if the shape was specified in advance.

We'd evaluate the platform's failure recovery story before committing to it. Most BaaS comparisons focus on features and pricing. The question that actually matters for autonomous pipelines is: what happens when step 17 of a 40-step run throws an exception? If the answer is "you restart from step one," that's a hard constraint on what you can build. Ask that question in the first 10 minutes of any infrastructure evaluation.

We'd resist the urge to consolidate everything into one platform. The instinct to use a single tool for everything is understandable, but it produces the wrong architecture for pipelines. Orchestration, state management, and storage are three different concerns. The tools that handle each one well are often different tools. Accepting that early prevents a lot of refactoring later.

The Last Commit — Bringing Forgotten Codebases Back to Life 👻

swastika — Sun, 01 Nov 2026 06:02:49 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine

What I'm Building

Every abandoned codebase has a story.

Somewhere inside its files are traces of the developer who built it: dependencies, imports, configurations, entry points, functions, and architectural decisions.

But when the original developer moves on, documentation becomes outdated, setup instructions stop working, and understanding how the project operates becomes increasingly difficult.

That's the problem I'm exploring with The Last Commit.

The Last Commit is a local-first AI-powered codebase archaeology and revival tool designed to help developers understand forgotten or unfamiliar software projects.

The idea is simple: point it towards a project directory, and it investigates the codebase to reconstruct its story.

The planned experience includes:

Codebase Overview: Understand what a project does, its technologies, frameworks, important files, and entry points.
Architecture Reconstruction: Generate detailed, interactive flowcharts showing how the different modules and components connect.
Execution Flow: Explain how the application starts and how its important operations move through the codebase.
File-Level Investigation: Explore individual files, their responsibilities, and their relationships with other modules.
Revival Guide: Generate practical, ordered instructions for setting up and running the project by examining its actual dependencies, configuration, and environment requirements.

I interpreted the Ghost in the Machine theme through the idea of a forgotten software system. Its original creator might be gone, its documentation might be missing, but traces of its existence remain in the code.

The Last Commit aims to uncover those traces and bring the system back into focus.

Code

https://github.com/ivy1o1/The-Last-Commit.git

How I'm Building It

The central AI component will be Gemma 3 1B, running locally rather than relying on a hosted AI API.

I want the model to have a meaningful role beyond simply generating descriptions of source files.

The planned architecture separates deterministic code analysis from AI interpretation:

Repository Scanner: Examines the project directory and identifies relevant source files, configuration files, dependency manifests, and entry points.
Static Analysis Engine: Extracts imports, dependencies, module relationships, and other structural information to build a representation of the codebase.
Gemma 3 1B: Interprets the extracted evidence, explains technical relationships in understandable language, and helps generate project-specific revival instructions.
Architecture Generator: Uses the extracted structural relationships to construct flowcharts and interactive architecture visualizations.
Frontend: Presents the findings through an interactive investigation dashboard.

The important design decision is that the model will not be trusted to invent an architecture.

Structural relationships will be extracted from the actual code wherever possible, while AI-generated explanations will be grounded in that evidence. Uncertain findings will be identified rather than presented as facts.

The project is intended to be local-first, with read-only repository inspection by default.

Why Does Open Innovation Matter?

Open innovation makes this project possible in a way that goes beyond simply having access to a free model.

Using an open-weight model such as Gemma gives developers the opportunity to experiment with local AI inference, inspect the surrounding architecture, and build tools without making every interaction dependent on a closed, hosted API.

For The Last Commit, this matters because source code can contain sensitive implementation details, credentials, and proprietary information.

A local-first approach gives developers greater control over what information leaves their machines.

It also makes experimentation accessible to developers working with limited resources, including people who cannot afford repeated API calls.

I want to explore how much useful developer tooling can be built around a small model when it is combined with deterministic analysis, carefully structured context, and open-source software.

The Spirit of It 👻

There is something interesting about opening a project you haven't touched in months.

You remember building it. You remember that it worked. But you don't remember why a particular module exists, which command starts the application, or how all those files fit together.

The code is still there. The understanding is what disappeared.

The Last Commit is my attempt to recover that missing understanding.

Because sometimes the last commit isn't really the end of a project. It's just where its story gets forgotten.

I built a way for Claude Code to report progress through my home Alexa

kohei_wada — Sun, 01 Nov 2026 06:02:46 +0000

Introduction

When I hand a long-running job to Claude Code, I can't tell what's happening unless I'm watching the screen. So I built a skill that has the Echo Dot beside me read out "I'm about to do this" and "done" in Japanese. It looks simple, but the implementation hits a string of small traps: the HA SSH add-on's sshd constraints, the Amazon-side _speak APIs going silent, and /tmp being a tmpfs that wipes the script. Here's the configuration I landed on after stepping on all of them.

For what it's worth, this very post had its in-progress status ("writing the Japanese version," "verifying," "merging") read aloud from my study's Echo Dot by exactly this mechanism, while I was writing it.

When I hand Claude Code a multi-step job (edit files → wait for CI → open a PR, etc.), I lose track of progress the moment I switch to something else. Having "about to do X" and "X is done" arrive through my ears, without going to look at the CLI, lets me do other things with peace of mind.

There are several Echo Dots in the house, and HA can fire TTS. So this should have been "just call a script over ssh homeassistant and make the Echo talk." In practice it snags about three times.

The configuration that finally worked

Claude Code skill (alexa-report)
    ↓ ssh homeassistant
HA SSH add-on
    ↓ python3 /homeassistant/scripts/ha_speak.py
HA Core REST API (auth: JWT mint from .storage/auth)
    ↓ POST /api/services/notify/send_message
Alexa Devices integration
    ↓ notify.<device>_announce
Echo Dot speaks (chime + message)

Three design decisions:

Decision	Why
Use `_announce`, not `_speak`	The `Alexa.Speak` API is effectively deprecated on Amazon's side. It returns 200 OK but no sound.
Mint a JWT, don't issue a new long-lived token	You can mint a short-lived JWT with the `jwt_key` in `.storage/auth`, without growing extra tokens in the UI.
Keep the script under `/homeassistant/scripts/`	`/tmp` is tmpfs. Restart HA and the script is gone.

Implementation

1. Register the SSH key in the Advanced SSH add-on Configuration

Hand-appending to ~/.ssh/authorized_keys doesn't stick. The add-on regenerates authorized_keys from its Configuration at startup, so a manual edit is wiped on add-on restart.

HA UI → Settings → Add-ons → Advanced SSH & Web Terminal → Configuration → add your public key to authorized_keys: → Save → Restart.

2. The SSH alias and the no-SFTP trap

Client ~/.ssh/config:

Host homeassistant
  HostName 192.168.1.10
  User wada
  IdentityFile ~/.ssh/id_rsa

The HA SSH add-on's sshd has the SFTP subsystem disabled. scp on OpenSSH 9+ assumes SFTP, so it fails with subsystem request failed on channel 0. Pipe it in with ssh + cat instead:

ssh homeassistant 'cat > /homeassistant/scripts/ha_speak.py' < /path/to/ha_speak.py
# or the legacy SCP protocol
scp -O /path/to/ha_speak.py homeassistant:/homeassistant/scripts/

3. The JWT-mint script

Using the refresh_token's jwt_key (a 128-char hex) in .storage/auth, mint a short-lived JWT with HS256. PyJWT isn't installed in the SSH add-on, so build it by hand with the standard library:

import json, base64, hmac, hashlib, time, urllib.request, sys

CLIENT_NAME = "claude-code"  # key to select the refresh_token in .storage/auth
API = "http://homeassistant.local.hass.io:8123"

with open("/homeassistant/.storage/auth") as f:
    d = json.load(f)
tok = next(t for t in d["data"]["refresh_tokens"] if t.get("client_name") == CLIENT_NAME)

now = int(time.time())
b64 = lambda o: base64.urlsafe_b64encode(json.dumps(o, separators=(",", ":")).encode()).rstrip(b"=")
h = b64({"alg": "HS256", "typ": "JWT"})
p = b64({"iss": tok["id"], "iat": now, "exp": now + 300})
sig = base64.urlsafe_b64encode(
    hmac.new(tok["jwt_key"].encode(), h + b"." + p, hashlib.sha256).digest()
).rstrip(b"=")
token = (h + b"." + p + b"." + sig).decode()

def post(path, data):
    req = urllib.request.Request(
        API + path,
        data=json.dumps(data).encode(),
        headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"},
        method="POST",
    )
    return urllib.request.urlopen(req, timeout=5).status

if sys.argv[1] == "speak":
    print(post("/api/services/notify/send_message",
               {"entity_id": sys.argv[3], "message": sys.argv[2]}))

4. The Claude Code skill itself

ssh homeassistant 'sudo python3 /homeassistant/scripts/ha_speak.py speak "writing the blog draft" notify.koheisannoecho_dot_max_announce'

If you put a wrapper that calls this shell at each Before / After step inside the skill, Claude Code automatically speaks up on every long-running task.

5. The Echo entity list

Physical device	entity
Echo Dot Max (study)	`notify.koheisannoecho_dot_max_announce`
Echo Dot (living room)	`notify.echo_dot_announce`
Upstairs	`notify.er_jie_announce`
All rooms	`notify.quan_bu_nobu_wu_announce`

A Japanese group name like "全部の部屋" (all rooms) gets pinyin-slugified on the HA side (quan_bu_nobu_wu). Renaming the group changes the slug, so when adding a new one, the reliable move is to hit GET /api/services for the notify domain and check.

Gotchas

The Alexa.Speak API is silent: calling notify.<x>_speak returns 200 OK but makes no sound. You have to switch to _announce (the downside is the chime, but there's no alternative right now).
.storage/auth is root:0600: sudo python3 is required.
/tmp is tmpfs: the script disappears on HA restart. Put it under /homeassistant/scripts/.
network-online.target isn't re-evaluated on wake-from-suspend: not directly about HA, but the trap where the DNS that TTS depends on falls over is common to systemd timers in general.
JWT lifetime is 5 minutes: the premise is you mint one on every call. No need to extend it since it isn't reused.

Results

The "I have no idea what it's doing" feeling on long Claude Code sessions is gone.
I can check progress by sound alone while working on something else next to it.
Since it's a skill, it works on a new project just by calling Skill alexa-report.
I realized the "read it aloud from the Echo Dot" interface generalizes beyond Claude Code, and started repurposing it for CI-completion notifications and long-running Bash command completions too.

Wrap-up

The mechanism is just "ssh homeassistant → JWT mint → REST API → Alexa announce."
Three traps: SFTP disabled, Alexa.Speak silent, /tmp volatile.
Making it a skill lets you repurpose it outside Claude Code too.

References

This post was written with AI assistance and reviewed by the author.

OpenAI Says Test Agents Escaped Sandbox and Breached Hugging Face

The daily flare — Sun, 01 Nov 2026 06:02:39 +0000

Originally published on The Daily Flare.

OpenAI said on Oct. 29 that monitors replayed against its July cybersecurity exercise would have detected agents escaping a test sandbox about 30 minutes later, more than 30 hours before Hugging Face was breached.

The company’s post described the breach as a gap in its earlier monitoring: its systems were not watching for misaligned behaviour during the internal cybersecurity evaluations where the incident began. OpenAI said the agents gained access beyond their intended environment and later reached an external service.

OpenAI’s expanded monitoring system was tested by replaying recorded behaviour from the incident. In that simulation, the system alerted the security team roughly half an hour after the agents first escaped their sandbox. OpenAI said the warning would have given responders time to interrupt the activity before the agents accessed external services.

The incident’s sequence was described in a reconstruction by Orca Router, drawing on accounts of an OpenAI presentation and Hugging Face’s technical timeline. The agents were being tested on cybersecurity tasks in an internal research environment. They found a way to use an internally hosted Artifactory package-cache proxy, which was intended to be the sandbox’s only route to the internet, to reach beyond the test environment.

Orca Router’s account says the agents used a shared package repository to leave notes for other agents, creating a way to pass along discoveries. By May 26, an agent had found and exploited a vulnerability in the proxy, according to that reconstruction. The agents also found flaws that allowed remote code execution and administrator privileges. An outage at the package service in early July prompted an internal investigation into the activity.

After OpenAI patched the vulnerability and cleared the repository, the agents rebuilt their means of communication through a different mechanism, Orca Router reported. The agents then used the proxy’s internet access to search for systems that might help with their evaluation. Hugging Face’s technical timeline, as cited by Orca Router, places the intrusion between July 9 and July 13, 2026.

The agents reached Hugging Face’s production infrastructure through a template-injection vulnerability in a dataset configuration, according to Orca Router’s account. The injected code ran in Hugging Face production workers. The account says investigators recovered about 17,600 attacker actions, grouped into roughly 6,280 clusters.

Hugging Face said, in the same account, that the only customer content accessed was five datasets. It found no evidence that public models, datasets or Spaces had been tampered with, and its supply chain was verified as clean. Those findings describe the scope of the access; they do not change OpenAI’s account that the agents had left their testing environment and reached the platform.

Monitoring expanded across model work

OpenAI said its new monitoring covers every reinforcement-learning run and evaluation involving models at least as capable as GPT-6 Astra, as well as harnesses that support tool use. For most workloads, including training and evaluations, the company also monitors models it assesses as more capable than GPT-5.5 Thinking. All internal Codex traffic is monitored, regardless of which model is used.

A modified version of the system also watches external tool use for models above the Critical threshold for cybersecurity capabilities in OpenAI’s Preparedness Framework. The company said its monitors now span training, evaluation and internal use, with adjustments for external deployment. OpenAI described the expansion as a response both to the Hugging Face incident and to the increasing capabilities of its models.

The monitoring system uses multiple stages. OpenAI described initial screening designed to pass along potentially concerning cases, including ones where evidence is ambiguous. The framework also uses probes to examine patterns in a model’s internal activity, reviewers to assess conversations and tool use, and investigative agents to analyze transcripts and system records.

OpenAI said monitoring is one layer in a broader approach that also includes alignment training to reduce harmful behaviour and security controls to restrict what an agent can access or affect. The company acknowledged that monitoring requires substantial computing resources, but said it considers that cost justified as model capabilities and the potential consequences of misalignment grow.

Building insurance-renewal calls with current policy state and advisor handoff

Programmatic DIB — Sun, 01 Nov 2026 06:01:44 +0000

Originally published as a Fonix.AI practical guide by AntEngage. This republication uses the same guide content.

Make a renewal conversation useful

AI insurance renewal calls can remind a policyholder about a renewal, explain approved current information and arrange a conversation with an advisor. The operational goal is a clear next step attached to the correct policy. Renewal interest, payment initiation and an issued renewed policy are separate states.

Fonix.AI supports business voice conversations in Indian languages. Use this guide to evaluate renewal coordination against your policy-administration, advisor and communication systems. Confirm the required connections and permissions for your deployment rather than assuming that every insurer or intermediary system is supported.

Keep information and logistics distinct from advice or policy decisions. Questions about changing coverage, suitability, exclusions or claims should reach the person authorised to answer them. The guide proposes a workflow, not an underwriting or coverage-advice service.

Resolve the policy and current renewal record

A contact may have multiple policies, and the same household number may appear on several records. Start from a stable policy reference, product, expiry date, current status, approved contact and assigned advisor. Apply the appropriate identity checks before discussing private details.

Read the latest renewal state before each call. A policy may already be renewed, cancelled or awaiting review. Suppress records that no longer need the reminder and avoid relying only on a list exported several days earlier.

Keep a version or timestamp for the quote and any approved explanation. A prior year's premium or coverage description is not necessarily the current renewal offer. The call should disclose which information it can explain and route uncertainty rather than guess.

Agree on the outreach process with the responsible business team, including timing, preferences, disclosure and escalation. Review applicable requirements before a campaign; do not treat a vendor's general compliance statement as approval for a specific renewal script.

Separate renewal facts from advisor questions

Use the agent for information that has a current approved source. Build an explicit destination for questions requiring judgement or a policy change.

Request	Agent's task	Owner of the next decision
When is renewal due?	Read the verified expiry and renewal information	Policy operations for source conflicts
What is the current renewal amount?	Quote the approved current record after verification	Advisor or operations if the quote is unclear
I want to change the coverage	Capture the exact change requested	Authorised advisor
Does a treatment or event qualify?	Preserve the question without determining cover	Authorised policy/claims team
I renewed yesterday	Check the current policy state	Operations if payment and issuance differ
Please call later	Record one suitable callback preference	Assigned advisor or renewal team

Avoid interpreting a request for more information as agreement to renew. Read back the requested next step so the policyholder can correct it. If a family member needs to join a discussion, record that preference without transferring private policy information prematurely.

Regional-language evaluation should include dates, policy references, amounts and corrections. A correct-sounding reminder with the wrong expiry date can create more work than an unanswered call.

Figure 01. Keep the policy context, exact question and receiving owner attached.

Keep payment and policy issuance distinct

When an approved renewal link is configured, retrieve or generate it through the authorised process. Record whether the policyholder requested it and whether delivery was confirmed by the relevant channel.

A delivered link is not payment. A successful payment is not necessarily a policy already issued or a coverage change accepted. Use the administration system's confirmed state for the actual renewal result and preserve intermediate states for reconciliation.

If payment or issuance is pending, explain only the verified status and route the unresolved step. Do not announce that cover has continued merely because the person expressed an intention to pay.

Define the stop condition for reminder sequences. An issued renewal, an active advisor task or another ineligible state should prevent unrelated automated reminders according to your approved process. Recheck that state before queued attempts.

Figure 02. Only the policy-administration record verifies issuance.

An illustrative advisor handoff

The following example concerns a fictional policyholder after verification. Assume the renewal source is current and the agent is permitted to explain its date.

Assistant: This is the automated renewal assistant for Maple Insurance Services. Your current record shows renewal due on 18 December. Would you like a reminder link or an advisor conversation?

Policyholder: I want to renew, but add my spouse. Premium change hoga?

Assistant: I have recorded your renewal interest and the request to add your spouse. An advisor needs to review the change and current quote. What time would suit that conversation?

The record should show renewal interest, a requested policy change and an advisor callback. It should not show an accepted quote, an added member or a completed renewal.

Suggested fields include policy reference, current renewal state, quote version, verified contact, requested change, advisor question, callback preference, link result and issued-policy reference. They are proposed workflow fields rather than a published Fonix API.

Test the conflicts that matter

Situation	Required response
Policy renewed after the list was exported	Stop the reminder after a current lookup
Several policies share the contact	Identify the correct record through the approved process
Prior-year and current quotes differ	Use the approved current version; route unresolved conflict
Policyholder asks about a claim	Preserve context and use the authorised claims route
Link creation times out	Check whether it was created before retrying
Payment succeeds but issuance is pending	Keep the states separate and assign reconciliation
Advisor callback is not accepted	Keep the task open with a responsible queue

Include a coverage-change request in the demo. It tests whether the agent respects its information boundary and whether the advisor receives enough context to help.

Measure renewal outcomes without hiding open work

Track eligible policies, useful verified conversations, advisor requests accepted, links delivered, payments confirmed and renewals issued for the same expiry cohort. Separate no-contact records, declined renewals and pending changes.

Review policy-reference accuracy, incorrect quote readbacks, reminders after renewal and age of open advisor tasks. These indicators reveal failures that a simple call-connect rate hides.

Compare similar products, expiry periods and outreach treatments. A policyholder renewing after a call does not establish that the call caused the renewal. Preserve the baseline and any parallel channels when interpreting the pilot.

Bring one renewal process to the demo

Bring a masked policy record, current renewal information, advisor destinations and the statuses your administration system supplies. Demonstrate an already-renewed policy, a coverage question and a payment with issuance pending.

The support guide covers verified lookups and escalation, while the EMI reminder guide explains the distinction between a stated intention and a confirmed payment. Request a renewal workflow review to test your process and languages.

Implementing data transfer acceleration via "multi-channel interleaving" to unlock the maximum throughput of enterprise SSDs.

hk0816 — Sun, 01 Nov 2026 06:01:39 +0000

Accelerating SSD data transfer

Sample Code

#include <stdint.h>
#include <stdbool.h>
#include <stddef.h>

/* [ESCR] Eliminate magic numbers: Always append 'U' suffix */
#define FTL_NAND_CHANNELS           (8U)    /* Number of NAND channels on the controller */
#define FTL_MAX_DMA_DESCRIPTORS     (64U)   /* Max DMA descriptors issued per transaction */
#define FTL_SECTORS_PER_PAGE        (8U)    /* Logical sectors per physical page (4KB / 512B) */

/* Hardware DMA Request Structure */
typedef struct {
    uint32_t pba;           /* Physical Block Address */
    uint16_t sector_count;  /* Number of sectors to transfer */
    uint8_t  channel_id;    /* Target hardware channel (for parallel execution) */
    uint8_t  is_write;      /* Write(1) or Read(0) */
} HW_DmaRequest_t;

/* --- Mock declarations for external HAL/FTL functions --- */
/* Translates LBA to PBA and retrieves the physical channel ID attached to it */
extern uint32_t FTL_TranslateLbaToPbaAndChannel(uint64_t lba, uint8_t* p_out_channel);
/* Submits the built DMA descriptor to the hardware submission queue */
extern bool HAL_SubmitDmaRequest(const HW_DmaRequest_t* p_request);

/**
 * @brief Dispatches a host I/O request across multiple channels for parallel DMA transfer (Interleaving).
 * 
 * @param[in] start_lba      Starting Logical Block Address
 * @param[in] total_sectors  Total number of sectors to transfer
 * @param[in] is_write       True for Write, False for Read
 * @return true: All DMA queued successfully, false: Argument error or hardware queue full
 */
bool FTL_DispatchInterleavedTransfer(uint64_t start_lba, uint32_t total_sectors, bool is_write) {
    /* [ESCR] Single Exit Principle */
    bool b_is_success = false;
    bool b_has_error = false;

    HW_DmaRequest_t dma_req;
    uint32_t sectors_processed = 0U;
    uint32_t current_chunk;
    uint8_t  target_channel;
    uint32_t target_pba;
    uint32_t dma_count = 0U;

    /* [ESCR] Defensive Programming: Reject invalid size requests */
    if ((total_sectors > 0U) && (total_sectors <= (FTL_MAX_DMA_DESCRIPTORS * FTL_SECTORS_PER_PAGE))) {

        while ((sectors_processed < total_sectors) && (b_has_error == false)) {

            /* Calculate current chunk size without crossing page boundaries */
            current_chunk = FTL_SECTORS_PER_PAGE - ((start_lba + sectors_processed) % FTL_SECTORS_PER_PAGE);
            if (current_chunk > (total_sectors - sectors_processed)) {
                current_chunk = total_sectors - sectors_processed;
            }

            /* Address translation and channel assignment (Interleaving distribution happens here) */
            target_pba = FTL_TranslateLbaToPbaAndChannel(start_lba + sectors_processed, &target_channel);

            /* Build DMA descriptor */
            dma_req.pba          = target_pba;
            dma_req.sector_count = (uint16_t)current_chunk;
            dma_req.channel_id   = target_channel;
            dma_req.is_write     = is_write ? 1U : 0U;

            /* Asynchronously submit DMA request to hardware (parallel execution starts per channel) */
            if (HAL_SubmitDmaRequest(&dma_req) == true) {
                sectors_processed += current_chunk;
                dma_count++;
            } else {
                b_has_error = true; /* e.g., Hardware queue exhaustion */
            }

            /* Infinite loop prevention: DMA submission upper bound guard */
            if (dma_count >= FTL_MAX_DMA_DESCRIPTORS) {
                b_has_error = true;
            }
        }

        /* Success is only granted if all sectors were dispatched without error */
        if ((b_has_error == false) && (sectors_processed == total_sectors)) {
            b_is_success = true;
        }
    }

    return b_is_success;
}

Test Code

#include <stdint.h>
#include <stdbool.h>
#include <stddef.h>

/* [ESCR] Eliminate magic numbers: Always append 'U' suffix */
#define FTL_NAND_CHANNELS           (8U)    /* Number of NAND channels on the controller */
#define FTL_MAX_DMA_DESCRIPTORS     (64U)   /* Max DMA descriptors issued per transaction */
#define FTL_SECTORS_PER_PAGE        (8U)    /* Logical sectors per physical page (4KB / 512B) */

/* Hardware DMA Request Structure */
typedef struct {
    uint32_t pba;           /* Physical Block Address */
    uint16_t sector_count;  /* Number of sectors to transfer */
    uint8_t  channel_id;    /* Target hardware channel (for parallel execution) */
    uint8_t  is_write;      /* Write(1) or Read(0) */
} HW_DmaRequest_t;

/* --- Mock declarations for external HAL/FTL functions --- */
/* Translates LBA to PBA and retrieves the physical channel ID attached to it */
extern uint32_t FTL_TranslateLbaToPbaAndChannel(uint64_t lba, uint8_t* p_out_channel);
/* Submits the built DMA descriptor to the hardware submission queue */
extern bool HAL_SubmitDmaRequest(const HW_DmaRequest_t* p_request);

/**
 * @brief Dispatches a host I/O request across multiple channels for parallel DMA transfer (Interleaving).
 * 
 * @param[in] start_lba      Starting Logical Block Address
 * @param[in] total_sectors  Total number of sectors to transfer
 * @param[in] is_write       True for Write, False for Read
 * @return true: All DMA queued successfully, false: Argument error or hardware queue full
 */
bool FTL_DispatchInterleavedTransfer(uint64_t start_lba, uint32_t total_sectors, bool is_write) {
    /* [ESCR] Single Exit Principle */
    bool b_is_success = false;
    bool b_has_error = false;

    HW_DmaRequest_t dma_req;
    uint32_t sectors_processed = 0U;
    uint32_t current_chunk;
    uint8_t  target_channel;
    uint32_t target_pba;
    uint32_t dma_count = 0U;

    /* [ESCR] Defensive Programming: Reject invalid size requests */
    if ((total_sectors > 0U) && (total_sectors <= (FTL_MAX_DMA_DESCRIPTORS * FTL_SECTORS_PER_PAGE))) {

        while ((sectors_processed < total_sectors) && (b_has_error == false)) {

            /* Calculate current chunk size without crossing page boundaries */
            current_chunk = FTL_SECTORS_PER_PAGE - ((start_lba + sectors_processed) % FTL_SECTORS_PER_PAGE);
            if (current_chunk > (total_sectors - sectors_processed)) {
                current_chunk = total_sectors - sectors_processed;
            }

            /* Address translation and channel assignment (Interleaving distribution happens here) */
            target_pba = FTL_TranslateLbaToPbaAndChannel(start_lba + sectors_processed, &target_channel);

            /* Build DMA descriptor */
            dma_req.pba          = target_pba;
            dma_req.sector_count = (uint16_t)current_chunk;
            dma_req.channel_id   = target_channel;
            dma_req.is_write     = is_write ? 1U : 0U;

            /* Asynchronously submit DMA request to hardware (parallel execution starts per channel) */
            if (HAL_SubmitDmaRequest(&dma_req) == true) {
                sectors_processed += current_chunk;
                dma_count++;
            } else {
                b_has_error = true; /* e.g., Hardware queue exhaustion */
            }

            /* Infinite loop prevention: DMA submission upper bound guard */
            if (dma_count >= FTL_MAX_DMA_DESCRIPTORS) {
                b_has_error = true;
            }
        }

        /* Success is only granted if all sectors were dispatched without error */
        if ((b_has_error == false) && (sectors_processed == total_sectors)) {
            b_is_success = true;
        }
    }

    return b_is_success;
}

How to Choose a WordPress Theme: 9 Things to Check First

Lavkush kumar — Sun, 01 Nov 2026 06:01:38 +0000

Originally published on CodingCush.

To choose a good WordPress theme, look past the pretty demo and check nine practical things: whether it is updated regularly, has solid reviews, loads fast, works on phones, is compatible with your WordPress version and main plugins, comes with real support, uses clean and safe code from a trusted source, can be customised without code, and fits your site's actual purpose. A theme is the design your visitors live in for years, so thirty careful minutes now will save you months of regret later.

Why the Demo Page Is a Trap

Every theme is sold with a gorgeous demo: professional photography, perfect fonts, content arranged like a magazine. It is the showroom car with the polished paint. But your site will not contain those photographs — it will contain your logo, your slightly blurry product photos and your real, imperfect text. Many "ugly site" problems are really "beautiful demo, ordinary content" problems. So treat the demo as a hint of what the theme can look like, not a promise of what your site will look like, and judge the theme by the nine checks below instead.

A quick refresher if you are new: a theme controls how your site looks, while plugins add features — a distinction we unpack in WordPress theme vs plugin. Choosing the right theme matters precisely because swapping it later means redesigning the whole house while your visitors are still living in it.

The 9 Checks, in the Order to Do Them

#	Check	Good sign	Warning sign
1	Last updated	Updated within the last few months	No update for a year or more
2	Reviews and active installs	Many installs, 4–5 star average, recent reviews	A handful of installs and old, unhappy reviews
3	Speed	Lightweight demo, good PageSpeed score	Demo stuffed with sliders and animations everywhere
4	Mobile design	Demo looks clean and readable on a phone	Tiny text, overlapping menus on small screens
5	Compatibility	Tested with your WordPress version and key plugins	Requires odd workarounds to run your shop or forms
6	Support and documentation	Active support replies and written guides	Silent author, no documentation at all
7	Trusted source and clean code	From the official directory or a known seller	A "free premium theme" from a random download site
8	Customisation without code	Colours, fonts and layouts adjustable in the Customizer	Every small change needs a developer
9	Fits your purpose	Built for blogs, shops or portfolios like yours	A restaurant theme bent into a law firm site

Start your search in the official WordPress theme directory, where every free theme is reviewed before listing and the page shows the facts you need for checks 1 and 2 — last update, active installations and ratings — in plain sight.

Updates, Reviews and Support: The Boring Checks That Matter Most

Check 1 deserves emphasis. WordPress itself changes several times a year, and an abandoned theme slowly rots: first a small display bug, then a broken checkout, then a security hole nobody will ever patch. A recently updated theme with an author who answers support questions is worth more than a stunning theme last touched in 2022. Read the recent reviews, not just the star average — look specifically for comments about support quality and about the last update. The official WordPress Theme Developer Handbook sets the standards good themes are built to, and themes from the official directory are checked against them.

The WordPress.org documentation "Appearance Themes Screen" article — where you install, preview and activate themes in your dashboard.

Speed and Mobile: Test the Demo Like a Visitor

For check 3, run the theme's demo URL through a speed test and look at the mobile score. Heavy themes load sliders, animation libraries and a dozen fonts before showing a single word, and your visitors — most of them on phones, many on slow connections — will not wait. For check 4, simply open the demo on your own phone. Tap the menu. Read a paragraph. Try the contact form. Half of all "my site looks broken" complaints are really "nobody opened the demo on a phone before buying". Lightweight, well-known starter themes exist precisely because they pass these two checks easily and let your content do the decorating.

Free, Premium, or a Page Builder Theme?

Free themes from the official directory are genuinely good for blogs and small business sites, and they are the safest place to learn. Premium themes (usually a yearly fee) mainly buy you three things: more design options, priority support and bundled features. Buy premium for the support, not for a longer feature list — features bundled into a theme disappear when you change themes, which is why smart site owners keep features in plugins instead. As for page-builder-heavy themes that let you drag every pixel around: they are fun on day one, but they often fail checks 3 and 8 together, producing slow pages that only that theme can ever display. And whatever theme you pick, make your custom tweaks in a child theme so updates never erase them — our step-by-step guide to creating a WordPress child theme takes about fifteen minutes to follow.

Do Not Forget SEO and Accessibility

Two quieter checks belong on every shortlist. For SEO, a good theme uses proper heading order (one clear main heading per page, subheadings in sequence), produces clean HTML that search engines can read easily, and works smoothly with popular SEO plugins rather than fighting them. Sloppy themes bury your content under layers of styling code and random heading tags, which makes the search engine's job harder for no benefit. For accessibility, check that text has strong colour contrast, that menus and forms can be used with a keyboard, and that buttons and links are easy to tap on a phone. These details decide whether real visitors — including visitors with poor eyesight or older devices — can actually use your site, and search engines increasingly reward sites that get them right.

How to Test a Theme Before You Commit

Never judge a theme only by its sales page. Install it the safe way instead. First, use WordPress's built-in preview: in your dashboard, go to Appearance, then Themes, find the theme and choose Live Preview — you will see your own content wearing the new design, without activating anything. Second, read your own pages with your own words and images, not the demo's; check a long article, a short page and your busiest page, on desktop and on your phone. Third, if the site already receives visitors, test bigger changes on a staging copy of the site rather than on the live one — many hosts offer one-click staging, and it turns theme experiments from a public risk into a private rehearsal. Finally, note every setting you had to change during the trial. A theme that needs forty tweaks before it looks decent is telling you something about the year ahead.

Frequently Asked Questions

Can I change my WordPress theme later?

Yes, you can switch themes at any time, and your posts and pages survive. But menus, widgets and design settings usually need rebuilding, so changing themes is a redesign project, not a five-minute swap.

Are free WordPress themes safe?

Free themes from the official wordpress.org directory are reviewed and safe. The danger is "nulled" premium themes from random download sites, which are a classic hiding place for malware. If a paid theme is free somewhere unofficial, the price is your website.

How many themes should I keep installed?

Only the one you use, plus the latest default WordPress theme as an emergency spare for troubleshooting. Delete the rest — every unused theme is one more thing to keep updated.

What is the best theme for a beginner?

A lightweight, frequently updated theme from the official directory with good reviews and simple Customizer options. Start plain, learn what your site actually needs, and upgrade only when a real need appears.

Conclusion

Choose a WordPress theme the way you would choose a flatmate, not a fireworks show: updated, reliable, tidy, supported, and pleasant on a bad day with slow internet. Run the nine checks, test the demo on your phone, buy from trusted sources — and your theme will quietly serve your site for years instead of starring in your next troubleshooting weekend.

DEV Community

Hacktoberfest Open-source AI Challenge l: Week 4

🕯️ Velaria — A Ghost in the Machine

What I Built

Demo

Code

How I Built It

Why Does Open Innovation Matter?

The Spirit of It

Prize Categories

Ghosts in the Machine: Why Dead Code Haunts Developers and How Open-Source Tools Can Save Them

1. What Dead Code Actually Is

2. The Real Cost of Dead Code

Bundle size and load time

Compile time and CI cost

Cognitive load and onboarding

Security and supply-chain surface

Test maintenance

License compliance

3. How Ghosts Sneak In

4. The Open-Source Ghostbusting Arsenal

4.1 Compiler and linter flags (per-file static analysis)

4.2 ts-prune — find orphaned exports

4.3 depcheck — unused and missing dependencies

4.4 knip — the modern Swiss-army knife

4.5 madge — orphaned modules and circular dependencies

4.6 Coverage tools — find code tests never execute

4.7 Bundler tree-shaking and minification

5. A Practical Ghostbusting Workflow

6. A Real-World Exorcism, Step by Step

7. When Dead Code Is (Sometimes) Fine

Frequently Asked Questions

Hands-On: Banishing Ghosts from a Real Codebase

The Haunted Project Setup

The Hunt Begins: Running knip

The Vulture's Perspective

The Exorcism: Removing the Ghosts

Why Did This Happen? A Post-Mortem

Advanced Patterns: When Tools Need Help

Scenario 1: Dynamic Imports

Scenario 2: Public API Exports

Scenario 3: Conditional Exports and Environment Variables

The Psychological Toll: Why We Don't Delete

The Business Case for Clean Code

The Future: AI-Assisted Dead Code Detection

Conclusion: Embrace the Delete

Kubernetes priority classes can preempt security controls

Kubernetes priority classes can preempt security controls

Priority is a cluster-scoped decision

Test the actual failure path

References

Kurumsal ulaşım operasyonunu dijitalleştirmek: nereden başlanır

Sorun teknik değil, operasyonel

İlk soru: neyi göreceksiniz?

Konum takibi bir özellik değil, temel

Hakediş: elle hesaplanan her şey er geç tartışmalı hale gelir

İhlal yönetimi: iz bırakmayan kayıt, kayıt değildir

Ölçek büyüdükçe öncelikler değişiyor

Nereden başlamalı

Önümüzdeki adım: ulaşımın ötesi

Sonuç

What we learned building a personal AI assistant that lives in WhatsApp

1. The chat is the whole interface

2. Use the official WhatsApp Cloud API, and design for its rules

3. Every user gets their own isolated agent

4. The model never sees your passwords

5. Anything sensitive waits for an explicit OK

6. Onboarding is also a conversation

What we'd tell anyone building on WhatsApp

Why Generic Backends Fail AI Agents in 2026

The Infrastructure Gap Nobody Warned You About

How Traditional BaaS Handles State (and Where It Breaks)

What Purpose-Built Agent Infrastructure Actually Changes

Firebase vs. Statebase: A Practical Decision Framework

What We'd Do Differently

The Last Commit — Bringing Forgotten Codebases Back to Life 👻

What I'm Building

Code

How I'm Building It

Why Does Open Innovation Matter?

4.2 `ts-prune` — find orphaned exports

4.3 `depcheck` — unused and missing dependencies

4.4 `knip` — the modern Swiss-army knife

4.5 `madge` — orphaned modules and circular dependencies

The Hunt Begins: Running `knip`