For years, Redux was the go-to for state management in React apps. 
Then came React Query (now TanStack Query) — and it changed how we handle server state forever.

But here's the confusion many developers face:

<blockquote>
“If I’m using React Query, do I still need Redux?” 
“Can Redux Toolkit replace React Query?” 
</blockquote>

Let’s clear the fog. 🌫️


<h2>
 
 
 🧠 First — Two Different Problems
</h2>

Before comparing them, it’s crucial to understand what each tool actually manages.

<div class="table-wrapper-paragraph"><table>
<thead>
<tr>
<th>Type of State</th>
<th>Description</th>
<th>Example</th>
</tr>
</thead>
<tbody>
<tr>
<td>Client State</td>
<td>Data that exists only in the UI or app memory.</td>
<td>Modal open/close, filters, active tab</td>
</tr>
<tr>
<td>Server State</td>
<td>Data fetched from APIs or databases, often cached.</td>
<td>Users list, product data, comments</td>
</tr>
</tbody>
</table></div>

<ul>
<li>🧩 Redux Toolkit (RTK) → best for client state
</li>
<li>🌐 React Query → best for server state
</li>
</ul>


<h2>
 
 
 🧩 Redux Toolkit Overview
</h2>

Redux Toolkit (RTK) is the official, modern way to write Redux — without the boilerplate.

It’s great for:

<ul>
<li>Local or global UI state</li>
<li>Caching small API responses</li>
<li>Complex app logic</li>
<li>Predictable, centralized state updates</li>
</ul>
<h3>
 
 
 Installation
</h3>


<div class="highlight js-code-highlight">
<pre class="highlight shell"><code>npm install @reduxjs/toolkit react-redux
</code></pre>

</div>

<h3>
 
 
 Example
</h3>


<div class="highlight js-code-highlight">
<pre class="highlight tsx"><code>import { createSlice, configureStore } from '@reduxjs/toolkit'
import { Provider, useSelector, useDispatch } from 'react-redux'

const counterSlice = createSlice({
 name: 'counter',
 initialState: { value: 0 },
 reducers: {
 increment: (state) =&gt; { state.value++ },
 },
})

const store = configureStore({
 reducer: { counter: counterSlice.reducer },
})

function Counter() {
 const count = useSelector((state) =&gt; state.counter.value)
 const dispatch = useDispatch()
 return (
 &lt;button onClick={() =&gt; dispatch(counterSlice.actions.increment())}&gt;
 Count: {count}
 &lt;/button&gt;
 )
}
</code></pre>

</div>

<h3>
 
 
 ✅ Pros
</h3>

<ul>
<li>Predictable, centralized store</li>
<li>Time-travel debugging and middleware</li>
<li>Immutable updates via Immer</li>
<li>Integration with async logic via <code>createAsyncThunk</code>
</li>
<li>Scales beautifully for enterprise apps</li>
</ul>
<h3>
 
 
 ⚠️ Cons
</h3>

<ul>
<li>Boilerplate compared to local hooks</li>
<li>Manual caching and invalidation for API data</li>
<li>More setup for smaller apps</li>
</ul>


<h2>
 
 
 🌐 React Query (TanStack Query) Overview
</h2>

React Query manages remote data fetching, caching, and syncing — not app logic.

It automates the painful parts of server-state management:

<ul>
<li>Fetching</li>
<li>Caching</li>
<li>Stale data invalidation</li>
<li>Background updates</li>
<li>Pagination &amp; optimistic updates</li>
</ul>
<h3>
 
 
 Installation
</h3>


<div class="highlight js-code-highlight">
<pre class="highlight shell"><code>npm install @tanstack/react-query
</code></pre>

</div>

<h3>
 
 
 Example
</h3>


<div class="highlight js-code-highlight">
<pre class="highlight tsx"><code>import { useQuery, QueryClient, QueryClientProvider } from '@tanstack/react-query'

const queryClient = new QueryClient()

function Todos() {
 const { data, error, isLoading } = useQuery({
 queryKey: ['todos'],
 queryFn: () =&gt; fetch('/api/todos').then(res =&gt; res.json())
 })

 if (isLoading) return &lt;p&gt;Loading...&lt;/p&gt;
 if (error) return &lt;p&gt;Error: {error.message}&lt;/p&gt;

 return (
 &lt;ul&gt;
 {data.map(todo =&gt; &lt;li key={todo.id}&gt;{todo.title}&lt;/li&gt;)}
 &lt;/ul&gt;
 )
}
</code></pre>

</div>

<h3>
 
 
 ✅ Pros
</h3>

<ul>
<li>Automatic caching and refetching</li>
<li>Handles background sync and retry logic</li>
<li>No reducers or actions — pure hooks</li>
<li>Amazing devtools for debugging</li>
<li>Ideal for data-heavy apps (dashboards, SaaS)</li>
</ul>
<h3>
 
 
 ⚠️ Cons
</h3>

<ul>
<li>Not designed for client UI state</li>
<li>No central store or global reducer logic</li>
<li>Limited control over global mutation side-effects</li>
</ul>


<h2>
 
 
 ⚔️ Head-to-Head Comparison
</h2>

<div class="table-wrapper-paragraph"><table>
<thead>
<tr>
<th>Feature</th>
<th>Redux Toolkit</th>
<th>React Query</th>
</tr>
</thead>
<tbody>
<tr>
<td>Primary Focus</td>
<td>Client/Global State</td>
<td>Server/Remote State</td>
</tr>
<tr>
<td>Data Source</td>
<td>Local or manual API calls</td>
<td>Fetched via queries</td>
</tr>
<tr>
<td>Caching</td>
<td>Manual</td>
<td>Automatic</td>
</tr>
<tr>
<td>Invalidation</td>
<td>Manual</td>
<td>Automatic</td>
</tr>
<tr>
<td>Setup Complexity</td>
<td>Moderate</td>
<td>Simple</td>
</tr>
<tr>
<td>Global Store</td>
<td>Yes</td>
<td>No</td>
</tr>
<tr>
<td>DevTools</td>
<td>Redux DevTools</td>
<td>React Query Devtools</td>
</tr>
<tr>
<td>Best For</td>
<td>State logic, app-wide control</td>
<td>Fetching &amp; syncing backend data</td>
</tr>
</tbody>
</table></div>


<h2>
 
 
 🔀 Can You Use Both Together?
</h2>

Absolutely — and many real-world apps do. 💪

They solve different concerns, and together they shine:

<ul>
<li>Use React Query for all server communication
</li>
<li>Use Redux Toolkit for UI and app logic
</li>
</ul>

Example integration: 


<div class="highlight js-code-highlight">
<pre class="highlight tsx"><code>// react-query handles data
const { data: todos } = useQuery(['todos'], fetchTodos)

// redux handles UI
const filter = useSelector(state =&gt; state.filter.value)
const filteredTodos = todos?.filter(todo =&gt; todo.status === filter)
</code></pre>

</div>



This approach keeps your data layer and UI layer cleanly separated.




<h2>
 
 
 🧩 TL;DR
</h2>

<div class="table-wrapper-paragraph"><table>
<thead>
<tr>
<th>You Need...</th>
<th>Use</th>
</tr>
</thead>
<tbody>
<tr>
<td>To manage app-wide UI state</td>
<td>Redux Toolkit</td>
</tr>
<tr>
<td>To handle API fetching and caching</td>
<td>React Query</td>
</tr>
<tr>
<td>To replace Redux entirely</td>
<td>React Query + local state</td>
</tr>
<tr>
<td>To handle everything with structure</td>
<td>Combine both</td>
</tr>
</tbody>
</table></div>




<h2>
 
 
 💬 Final Thoughts
</h2>

Redux Toolkit and React Query aren’t competitors — they’re complements. 
Think of them like this:

<blockquote>
🧠 Redux: Your app’s brain (logic, actions, state) 
🌍 React Query: Your app’s bloodstream (data flow and sync)
</blockquote>

Use the right tool for the right job — and your React app will stay clean, fast, and predictable.




If you liked this post, follow me for more deep dives on React architecture and state management! 
👉 <a href="https://dev.to/taronvardanyan">@taronvardanyan </a>

تناقش المقالة أدوار Redux Toolkit وReact Query (TanStack Query) في إدارة الحالة لتطبيقات React. يُستخدم Redux تقليديًا لإدارة حالة العميل، بينما تم تصميم React Query لحالة الخادم. غالبًا ما يتساءل المطورون عما إذا كانوا بحاجة إلى كلا الأداتين أو إذا كان يمكن لأحدهما استبدال الآخر، مما يبرز أهمية فهم وظائفهما المتميزة.

El artículo discute los roles de Redux Toolkit y React Query (TanStack Query) en la gestión del estado de aplicaciones React. Redux se utiliza tradicionalmente para gestionar el estado del cliente, mientras que React Query está diseñado para el estado del servidor. Los desarrolladores a menudo se preguntan si necesitan ambas herramientas o si una puede reemplazar a la otra, lo que resalta la importancia de entender sus funcionalidades distintas.

L'article traite des rôles de Redux Toolkit et de React Query (TanStack Query) dans la gestion d'état des applications React. Redux est traditionnellement utilisé pour gérer l'état client, tandis que React Query est conçu pour l'état serveur. Les développeurs se demandent souvent s'ils ont besoin des deux outils ou si l'un peut remplacer l'autre, soulignant l'importance de comprendre leurs fonctionnalités distinctes.

The article discusses the roles of Redux Toolkit and React Query (TanStack Query) in state management for React applications. Redux is traditionally used for managing client state, while React Query is designed for server state. Developers often question whether they need both tools or if one can replace the other, highlighting the importance of understanding their distinct functionalities.

Redux Toolkit vs React Query: Do You Really Need Both? 🤔

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<h1>
 
 
 AI Amnesia: Erasing Knowledge Without a Trace
</h1>

Imagine your AI model accidentally learned something it shouldn't have – sensitive customer data, for example. Current methods for deleting this information often require retraining the entire model, an expensive and time-consuming process. What if we could surgically remove that knowledge without starting from scratch?

The key lies in a novel approach: creating artificial "forgetting cues." We're teaching the model to unlearn specific data patterns by exposing it to carefully crafted synthetic examples. These examples are designed to strongly contradict the information we want the model to forget, effectively overwriting the problematic associations in its memory. This works even if you don’t have access to the original data you need to erase.

Think of it like this: you're trying to forget a bad song stuck in your head. Instead of trying to actively suppress it (which rarely works), you blast an even more catchy song. The new song overwrites the old one, effectively erasing it from your mental playlist.

Benefits of Selective Forgetting:

<ul>
<li> Enhanced Data Privacy: Remove sensitive data without compromising the overall model's performance.</li>
<li> Reduced Retraining Costs: Avoid full model retraining, saving significant time and resources.</li>
<li> Improved Model Security: Eliminate vulnerabilities introduced by unintentionally learned patterns.</li>
<li> Adaptable Learning: Enables continuous refinement of AI models based on evolving data landscapes.</li>
<li> Compliance Ready: Supports compliance with data privacy regulations like GDPR.</li>
<li> Scalable Solutions: Works efficiently even with limited access to training data.</li>
</ul>

Practical Tip: One challenge is ensuring the synthetic data accurately targets the information you want to remove without negatively impacting the model's ability to generalize. Rigorous testing and validation with holdout datasets are crucial.

The promise of AI that can truly 'forget' opens exciting possibilities for responsible AI development. By enabling precise data deletion, we pave the way for more secure, compliant, and adaptable machine learning systems. Imagine AI models that can adapt to changing ethical guidelines or quickly unlearn incorrect information, all without massive retraining efforts. This is a crucial step towards trustworthy and responsible AI that respects data privacy and aligns with societal values. Future exploration could include extending this to different data modalities and model architectures.

Related Keywords: machine unlearning, data privacy, few-shot learning, zero-shot learning, synthetic data, model editing, catastrophic forgetting, incremental learning, continual learning, deep learning, neural networks, data security, algorithmic fairness, responsible ai, ethical ai, federated unlearning, privacy-preserving ai, model retraining, AI governance, data deletion, GDPR compliance

يتناول المقال نهجًا جديدًا لمعالجة مشكلة نسيان الذكاء الاصطناعي، حيث قد تحتفظ نماذج الذكاء الاصطناعي عن غير قصد بمعلومات حساسة. تتطلب الطرق التقليدية لحذف هذه البيانات غالبًا إعادة تدريب كاملة للنموذج، وهو ما يعد مكلفًا ويستغرق وقتًا. تتضمن التقنية الجديدة إنشاء 'إشارات نسيان' اصطناعية تساعد النموذج على نسيان أنماط بيانات معينة من خلال تعريضه لأمثلة صناعية تتناقض مع المعلومات غير المرغوب فيها، مما يسمح بإزالة المعرفة المستهدفة دون الحاجة للوصول إلى البيانات الأصلية.

El artículo aborda un nuevo enfoque para tratar el problema de la amnesia de la IA, donde los modelos de IA pueden retener inadvertidamente información sensible. Los métodos tradicionales para eliminar estos datos a menudo requieren un reentrenamiento completo del modelo, lo que es costoso y lleva tiempo. La nueva técnica implica crear 'señales de olvido' artificiales que ayudan al modelo a desaprender patrones de datos específicos al exponerlo a ejemplos sintéticos que contradicen la información no deseada, permitiendo así la eliminación selectiva de conocimientos sin necesidad de acceder a l…

L'article traite d'une nouvelle approche pour résoudre le problème de l'amnésie de l'IA, où les modèles d'IA peuvent involontairement conserver des informations sensibles. Les méthodes traditionnelles pour supprimer ces données nécessitent souvent un réentraînement complet du modèle, ce qui est coûteux et long. La nouvelle technique consiste à créer des 'cues d'oubli' artificiels qui aident le modèle à désapprendre des schémas de données spécifiques en lui présentant des exemples synthétiques qui contredisent les informations indésirables, permettant ainsi une suppression ciblée des connaissan…

The article discusses a novel approach to address the issue of AI amnesia, where AI models may inadvertently retain sensitive information. Traditional methods for deleting such data often require complete retraining of the model, which is costly and time-consuming. The new technique involves creating artificial 'forgetting cues' that help the model unlearn specific data patterns by presenting it with synthetic examples that contradict the unwanted information, allowing for targeted knowledge removal without needing access to the original data.

AI Amnesia: Erasing Knowledge Without a Trace

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Investigadores han desarrollado modelos informáticos avanzados destinados a mejorar las predicciones para los procesos de molienda de biomasa. Esta innovación se centra en convertir materiales como astillas de madera, residuos de cultivos y residuos sólidos municipales en combustibles y productos químicos valiosos, lo que es crucial para mejorar la independencia energética del país. Se espera que los modelos optimicen la eficiencia del procesamiento de biomasa, contribuyendo así a soluciones energéticas sostenibles.

Des chercheurs ont développé des modèles informatiques avancés visant à améliorer les prévisions pour les processus de broyage de la biomasse. Cette innovation se concentre sur la conversion de matériaux tels que les copeaux de bois, les résidus de culture et les déchets solides municipaux en combustibles et produits chimiques précieux, ce qui est crucial pour renforcer l'indépendance énergétique du pays. Les modèles devraient optimiser l'efficacité du traitement de la biomasse, contribuant ainsi à des solutions énergétiques durables.

Researchers have developed advanced computer models aimed at improving predictions for biomass milling processes. This innovation focuses on converting materials such as wood chips, crop residues, and municipal solid waste into valuable fuels and chemicals, which is crucial for enhancing energy independence in the country. The models are expected to optimize the efficiency of biomass processing, thereby contributing to sustainable energy solutions.

Researchers develop computer models for better biomass milling predictions

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Redux Toolkit vs React Query: Do You Really Need Both? 🤔

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