Hi, I’m Mehdi Fekih

August 22, 2026

Tarification à l’usage et économie des tokens IA, vers un nouveau paradigme pour l’intelligence artificielle API

Comment la facturation par token redéfinit l’accès, le coût et la stratégie des API d’intelligence artificielle

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L’intelligence artificielle bascule vers un nouveau modèle économique : l’API à la demande, tarifiée au token. Que l’on choisisse OpenAI, Anthropic, Google, Mistral ou l’on expérimente des plateformes d’agrégation comme OpenRouter, la tarification au token applique au monde de l’IA les recettes qui ont fait le succès du cloud. Ce nouveau standard bouleverse en profondeur la façon dont développeurs, entreprises et décideurs conçoivent l’accès à l’intelligence artificielle : flexibilité accrue, paiement à l’usage, choix concurrentiels, mais aussi nouveaux risques. Décryptage des mécanismes, atouts et challenges du paradigme « API IA », où chaque token devient décision stratégique.

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Du logiciel à l’API IA : naissance d’une nouvelle infrastructure

De la licence logicielle au cloud : une rupture définitive

L’histoire du numérique a été marquée par trois modèles dominants en vingt ans : la licence perpétuelle (logiciel sur site, paiement unique), l’SaaS avec abonnement, puis le cloud à la consommation. Désormais, l’intelligence artificielle ouvre une quatrième ère : celle de l’API IA facturée à l’usage, au token, qui combine la puissance du cloud et la flexibilité d’interfaces programmables.

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Modèle	Accès	Facturation	Infrastructure	Exemples
Licence perpétuelle	Sur site	Unique/annuelle	Serveurs internes	Microsoft Office, Photoshop
SaaS	En ligne	Abonnement	Cloud fournisseur	Salesforce, Slack
API IA par token	Programmable à la demande	À l’usage (token)	IA mutualisée cloud	OpenAI, Google Vertex AI, Mistral, Anthropic, xAI

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API IA : simplicité d’intégration, usage ajusté

L’API d’intelligence artificielle révolutionne l’accès aux LLM avancés : plus besoin de déployer ni gérer d’infrastructure lourde. Un endpoint, une clé d’authentification : en quelques minutes, l’IA enrichit une app, un chatbot, une plateforme métier.
La facturation « à la consommation » par token garantit une maîtrise immédiate des coûts : seul l’usage réel compte. Cette granularité s’accompagne de :

• Flexibilité : choix dynamique du modèle selon la tâche ou le budget.
• Implémentation rapide : ajout d’intelligence sans dettes techniques majeures.
• Mutualisation : partage des ressources sur un cloud opérant à très grande échelle.
• Évolutivité automatique : support de la montée en charge du service, sans anticipation matérielle.

« L’intelligence artificielle devient l’infrastructure invisible, comme l’électricité ou l’eau, mais à la granularité du token. »

Marché des API IA : une compétition accélérée par la standardisation

La généralisation de l’API IA par token stimule l’émergence d’un écosystème ultra-concurrentiel : OpenAI (ChatGPT, GPT-4), Anthropic (Claude 3), Google (Gemini, Vertex AI), Mistral (Fast, Large via OpenRouter), xAI (Grok)… Chacun propose désormais des APIs taillées pour la sélection à la demande, tandis que des plateformes multi-fournisseurs (OpenRouter, Together AI) permettent de combiner, comparer ou basculer de l’un à l’autre sans friction technique.

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Pour les entreprises, cette standardisation réduit le verrouillage technique et accélère l’expérimentation. Toutefois, l’agilité offerte par la logique du token implique une vigilance accrue : suivi des coûts en temps réel, gestion des quotas, dépendance au réseau et à la qualité des services sous-jacents — autant de nouveaux leviers stratégiques.

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Tarification au token : révolution des coûts et des usages

Unité de facturation : précision, prédictibilité et passage à l’échelle

La facturation à l’usage par token inscrit l’IA dans la lignée du « pay as you go » du cloud, avec une granularité extrême. Un token (environ 0,75 mot) devient la brique monétaire minimale de chaque appel API. Cela rend possible une mesure précise des coûts, adaptée du particulier à la multinationale.

• Paiement instantané et sans engagement : chaque prompt, chaque réponse, chaque pipeline a un coût lisible.
• Montée en charge fluide : du proof of concept à l’application grand public, la structure de coût suit l’usage effectif.
• Pression concurrentielle à la baisse : la compétition entre fournisseurs entraîne régulièrement une réduction du prix par token et encourage l’apparition d’alternatives (API open source, modèles auto-hébergés, etc.).

Comparatif : tarifs publics 2024 des principaux acteurs

Fournisseur / Modèle	Entrée (1k tokens)	Sortie (1k tokens)
OpenAI GPT-4o	0,005 $	0,015 $
Mistral Large	0,008 $	0,024 $
Anthropic Claude 3 Sonnet	0,003 $	0,015 $
Google Gemini 1.5 Pro	0,0025 $	0,0025 $
OpenRouter/agrégateurs	Selon modèle sous-jacent

Exemple concret : 10 000 requêtes/an avec 3 000 tokens par interaction coûtent ~900 $ sur GPT-4o, mais pourraient descendre à 120 $ avec un modèle interne Mistral Small.

Checklist : optimiser ses usages IA API au token

• Suivre en temps réel la consommation par type de prompt ou projet.
• Définir des plafonds de coûts mensuels ou par client.
• Rédiger des prompts compacts pour éviter l’explosion tarifaire liée à la verbosité.
• Comparer régulièrement les fournisseurs pour profiter de la guerre des prix.
• Automatiser l’alerting sur les dérives de consommation.

Avantages majeurs pour tous les profils métiers

• Innovation accessible : barrieres d’entrée basses, prototypage instantané.
• Évolutivité native : même modèle économique pour 10 ou 100 000 utilisateurs.
• Pas de gestion matérielle : ni serveurs, ni GPUs à anticiper.
• Mobilité contractuelle : migration ou diversification via APIs compatibles.
• Alignement immédiat coût/usage : dépenses proportionnelles à la valeur générée.

Limites et défis concrets

• Difficulté d’anticipation sur les usages massifs ou chaotiques.
• Nécessite une optimisation fine des prompts pour contenir la facture.
• Risque de « spike » (pics de charges), quotas imprévus, et interruptions de service si limites atteintes.

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Risques et limites de l’économie des tokens IA

Dépendance aux fournisseurs et verrouillage technologique

L’économie du token propulse les Goliaths technologiques (OpenAI, Google, Anthropic, xAI…) au centre du jeu. Si l’API promet l’agilité, elle peut induire un lock-in propriétaire : migration coûteuse, arrêt soudain d’un modèle ou hausse imprévisible des prix sont des risques réels. Hausse tarifaire brutale de GPT-4 en 2023 : de nombreuses startups ont été contraintes d’adapter leur stratégie ou de choisir des alternatives parfois moins performantes.

Volatilité des coûts et gouvernance des budgets

À l’usage massif, la facture API IA peut s’envoler et révéler une grande instabilité financière. Des changements tarifaires soudains ou une demande fluctuante rendent la planification difficile. Comme le souligne un CTO :

« Au-delà du million de tokens par jour, notre facture API varie parfois du simple au double selon la semaine. »

Enjeux de confidentialité et concentration du marché

Envoyer des données à des API externes soulève des doutes sur la confidentialité et la traçabilité. Pour les secteurs régulés (droit, santé…), des choix alternatifs émergent : modèles open source comme Mistral ou Llama, auto-hébergement, solutions de chiffrement avancé.

Tableau de synthèse : cartographie des principaux risques

Risque	Mécanisme	Alternatives/Pistes
Dépendance fournisseur	API fermées, modèles exclusifs	Open source : Mistral, Llama
Volatilité prix	Tarifs variables, quotas changeants	Plafonds, contractualisation long-terme
Confidentialité	Transit de données externes	Modèles auto-hébergés, chiffrement
Concentration du marché	Peu d’acteurs dominants	Plateformes multi-providers (OpenRouter)

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Face à ces risques, de nombreux professionnels privilégient une stratégie hybride : routage dynamique multi-fournisseurs et recours à des modèles locaux ou open source pour sécuriser la chaîne de valeur.

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Agents intelligents et avenir API : entre diversité et concentration

L’ère des agents IA multi-modèles

L’automatisation et l’orchestration intelligente de l’accès aux API IA posent les bases d’un nouveau meta-marché. Des agents IA désormais capables de router dynamiquement les requêtes vers différents modèles selon le coût, la qualité ou les exigences réglementaires. Des plateformes comme OpenRouter ou PromptLayer offrent un accès unifié aux moteurs d’OpenAI, Anthropic, Mistral ou Google Gemini, et permettent d’associer souplesse, contrôle budgétaire et gain de temps.

Checklist stratégique : tirer profit de la diversité IA API

• Flexibilité : router en temps réel sur le modèle le plus pertinent (performance, prix, conformité).
• Suivi centralisé des coûts : analytiques intégrées, plafonds, alertes de consommation.
• A/B testing instantané : expérimentation simplifiée sur les modèles concurrents.
• Réduction du lock-in technique : migration et agrégation de fournisseurs en un clic.

« L’agent IA devient l’équivalent du load balancer pour l’IA : il module dynamiquement les ressources, libérant les développeurs de la rigidité des choix d’infrastructure. »

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De la diversité à la concentration : nouveau verrouillage ?

Si l’aggrégation multi-modèles vise à réduire la dépendance, elle concentre le pouvoir entre les mains de quelques plateformes d’orchestration. Ces intermédiaires-clés, capables à terme d’imposer leurs propres règles ou surcoûts, peuvent constituer un pivot stratégique autant qu’une nouvelle source de verrouillage.

Avantages	Risques
Indépendance vis-à-vis d’un seul fournisseur	Capture possible du marché par quelques orchestrateurs
Adoption rapide des innovations IA	Imposition de règles tarifaires ou techniques
Comparaison transparente du coût par token	Concentration accrue sur l’accès centralisé

La diversité offerte par ces solutions est un moteur d’innovation, mais appelle à une veille stratégique permanente : garantir la souveraineté sur ses flux de données, maintenir la maîtrise budgétaire, anticiper les effets de concentration du marché.

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Conclusion

La tarification à l’usage par token redéfinit l’économie de l’IA, remplaçant la licence ou l’abonnement par le calcul à l’interaction. Ce nouveau paradigme, tout en apportant agilité, mutualisation et ouverture, exige rigueur et stratégie dans la gestion des coûts, le choix des partenaires et la maîtrise de la confidentialité. L’économie des tokens façonnera la décennie à venir, autant source d’opportunités que de nouveaux équilibres à inventer entre diversité, indépendance et efficacité.

August 12, 2026

Awesome Public Datasets: A Treasure Map for Data-Driven Projects

If you have ever started a machine learning project, built a dashboard, written a research paper, or simply wanted to explore real-world data, you already know the hardest part is often not the code. It is finding a good dataset.

That is why the GitHub repository Awesome Public Datasets is such a valuable resource. It is a curated collection of public datasets organized by topic, making it easier for developers, researchers, analysts, students, and data enthusiasts to discover high-quality data sources without spending hours searching across the web.

Whether you are looking for climate records, economic indicators, social network graphs, image datasets, government data, healthcare resources, or machine learning benchmarks, this repository acts like a map to the public data ecosystem.

What Is Awesome Public Datasets?

Awesome Public Datasets is an “awesome list” dedicated to topic-centric public data sources. Like other awesome lists on GitHub, its goal is simple: collect useful links in one place and organize them so people can find what they need quickly.

The repository includes datasets from a wide range of domains, including agriculture, biology, chemistry, climate and weather, cybersecurity, economics, education, energy, finance, GIS and geospatial data, government, healthcare, image processing, machine learning, natural language processing, neuroscience, physics, social sciences, software, sports, time series, and transportation.

It also includes complementary collections, which can lead users to even more dataset repositories and archives. In short, it is not a single dataset. It is a gateway to hundreds of datasets.

Why This Repository Is So Useful

The internet is full of data, but not all data is easy to find, clean, documented, or usable. Many valuable datasets are buried inside university pages, government portals, academic archives, old project websites, or research labs.

Awesome Public Datasets helps solve that discovery problem. Instead of searching Google for “free public dataset for network analysis” or “open agriculture data,” you can browse a categorized list and quickly find relevant sources.

For example, the repository points to well-known resources such as the Stanford Large Network Dataset Collection for graph and network research, Open Food Facts for food product data, NBER Patent Citations for economics and innovation research, DIMACS Road Networks Collection for transportation and graph algorithms, and many climate, biology, finance, and machine learning datasets.

Each listing usually includes a short description and a link to the original data source. Many entries also include metadata links from the repository’s companion project, apd-core.

A Dataset Directory for Many Audiences

For Data Scientists

Data scientists can use it to find datasets for exploratory analysis, predictive modeling, visualization, and portfolio projects. Instead of working with the same few beginner datasets repeatedly, they can explore more specialized real-world data.

For Machine Learning Engineers

Machine learning engineers can find benchmarks and domain-specific data for experimenting with models. Categories like image processing, natural language, time series, and cybersecurity are especially useful for ML workflows.

For Researchers

Researchers can discover public data sources related to biology, physics, neuroscience, social sciences, climate, economics, and more. The repository can serve as a starting point for literature reviews, reproducible experiments, or interdisciplinary research.

For Students

Students learning Python, R, SQL, data visualization, or statistics can use the repo to find project ideas. Real datasets make learning more meaningful because they contain imperfections, surprises, and domain context.

For Journalists and Analysts

Data journalists and analysts can use the collection to locate public-interest datasets, especially in government, economics, transportation, education, healthcare, and climate.

What Makes It Better Than a Random List of Links?

The strength of Awesome Public Datasets is not just that it contains many links. It is the organization and curation.

The datasets are grouped by domain, so browsing feels natural. If you are interested in geospatial analysis, you can jump to GIS. If you are researching transportation networks, you can explore Transportation or Complex Networks. If you are looking for NLP resources, there is a Natural Language section.

The repository also uses status icons for entries. Some links are marked as healthy, while others are marked as needing attention. That is important because public dataset links often break over time. Seeing that maintenance status gives users a quick signal about whether a resource may need verification.

Another important detail: the README notes that the repository is automatically generated by apd-core. Contributors are asked not to edit the generated README directly, but to contribute through the appropriate metadata workflow. That makes the project more structured than a hand-edited list.

Great Project Ideas Using Awesome Public Datasets

• Build a climate dashboard: Use climate and weather datasets to visualize temperature changes, rainfall patterns, or extreme weather events over time.
• Analyze transportation networks: Explore road network datasets or public transport data to study shortest paths, congestion, or urban accessibility.
• Create a food product explorer: Use Open Food Facts or other agriculture and food datasets to analyze nutrition, ingredients, product origins, or labeling trends.
• Study social networks: Use graph datasets from the social networks or complex networks sections to learn network analysis, centrality, community detection, and graph visualization.
• Practice time series forecasting: Find time series datasets related to energy, finance, climate, or transportation and build forecasting models.
• Train an image classification model: Browse image processing datasets and experiment with computer vision techniques.
• Investigate public policy questions: Government, education, economics, healthcare, and social science datasets can support projects around inequality, public spending, population trends, or policy outcomes.

If you want help turning one of these datasets into a production-ready pipeline — ETL, storage, or a deployed model — check out my data engineering services or get in touch.

A Few Things to Keep in Mind

Awesome Public Datasets is a directory, not a guarantee that every dataset is ready to use immediately.

Before starting a project, you should always check the dataset license, whether the data is free or requires payment, update frequency, file format, documentation quality, privacy or ethical considerations, whether the link is still active, and whether the dataset is suitable for commercial use.

The repository itself notes that most datasets are free, but some are not. That distinction matters, especially for production or commercial projects.

Also, because many datasets come from third-party sources, quality can vary. Some may be clean and well-documented, while others may require significant preprocessing.

Why Public Datasets Matter

Public datasets are one of the foundations of modern data work. They make research more transparent, help students learn by doing, allow developers to test ideas, and enable journalists and citizens to investigate important questions.

Open data also lowers the barrier to innovation. A student with a laptop can analyze climate trends. A developer can build a prototype with public transportation data. A researcher can compare results using shared benchmarks. A startup can validate an idea before collecting proprietary data.

Repositories like Awesome Public Datasets make that ecosystem easier to navigate.

Final Thoughts

Awesome Public Datasets is one of those GitHub repositories worth bookmarking immediately. It saves time, sparks ideas, and opens doors to data from dozens of fields.

If you are learning data science, building machine learning models, writing research, creating visualizations, or looking for your next portfolio project, this repository is an excellent place to start.

The next time you ask, “Where can I find a good dataset?”, start with Awesome Public Datasets. And if you need a hand turning that data into something real — a dashboard, a model, a pipeline — I do this for a living. Let’s talk.

August 13, 2026

Bitcoin and “Cyclicality”: Reassuring Myth or Serious Analysis?

For several years, much of the discussion around Bitcoin has rested on one central idea: cyclicality.
Halvings, four-year cycles, an “inevitable” bull run, and an equally expected bear market. The narrative is well-oiled, almost comforting.

But one question deserves to be asked plainly:
Can we really call it serious analysis when we are waiting for a phenomenon that is supposedly obvious and predictable?

What supporters of cyclicality argue

Defenders of this view mainly rely on three elements:

• Historical data: since 2012, the major bullish phases have followed halvings.
• Programmed scarcity: the reduction in issuance is supposed to mechanically influence price.
• The repetition of human behavior: euphoria, excess, correction, forgetting, then return.

Taken individually, these elements are not absurd. The problem begins when they are presented as an almost deterministic mechanism.

Where the reasoning becomes fragile

1. A ridiculously small statistical sample

Bitcoin has existed for just over fifteen years.
Speaking of robust cycles based on three or four occurrences is closer to storytelling than science.

In finance, no one would describe that as a usable time series with a high level of confidence.

2. Confusing correlation with causation

The fact that rallies followed halvings does not prove that:

• the halving is their main cause,
• or that the same pattern will repeat identically.

The markets of 2013, 2017, and 2021 had nothing in common in terms of liquidity, participants, regulation, or macroeconomics.

3. A self-fulfilling prophecy

The more an idea is repeated, the more it influences behavior.

• Investors buy “before the halving”
• The media amplify the narrative
• Flows become synchronized

The cycle then becomes a social artifact, not a market law.
It works… until the day it no longer does.

Technical analysis: tool or illusion of control?

Technical analysis is not useless in itself. It is effective for:

• reading collective behavior,
• identifying liquidity zones,
• managing short- or medium-term risk.

But it does not turn an asset as young, political, and narrative-driven as Bitcoin into a predictable metronome.

Believing otherwise means confusing reading the past with the ability to forecast.

What Bitcoin really is

Bitcoin is not:

• a stock with cash flows,
• a traditional commodity,
• a mature asset.

It is all at once:

• a technological object,
• an experimental monetary asset,
• an ideological symbol,
• a field for global speculation.

Reducing all of that to a simple cyclical curve is intellectually comfortable, but analytically poor.

So, is skepticism a mistake?

No.

Being skeptical of cyclicality presented as obvious is, on the contrary, a way to:

• reject overly neat narratives,
• avoid lazy certainties,
• maintain an open analytical stance.

The real danger is not doubting cycles.
The real danger is mistaking them for natural laws.

Conclusion

Bitcoin cyclicality is a useful narrative, sometimes effective, but never guaranteed.
It helps structure expectations, not predict the future.

In a market this young and shifting, the only truly rational position remains intellectual caution.

The day the “obvious” cycle fails, it will not be an anomaly.
It will simply be the market reminding us that it owes nothing to our charts.

February 10, 2026

Configuring Amazon S3 Access Keys Securely for UpdraftPlus (WordPress)

Using Amazon Web Services S3 as remote storage for UpdraftPlus is a common and reliable approach for backing up a WordPress site.
However, many users are confused when AWS warns against long-term access keys and suggests alternative authentication methods.

This article explains the correct and secure way to configure S3 access for UpdraftPlus, why AWS shows those warnings, and what best practices actually apply in a real WordPress environment.

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Why AWS Warns About Long-Term Access Keys

AWS strongly encourages modern authentication mechanisms such as:

• IAM Roles
• Temporary credentials (STS)
• Workload identity federation

These are excellent practices when your application runs inside AWS (EC2, ECS, Lambda).

However, classic WordPress hosting does not support IAM roles.

If your WordPress site runs on:

• Shared hosting
• A VPS (DigitalOcean, OVH, Hetzner, etc.)
• On-premise infrastructure

then access keys are the only supported and correct solution.

AWS warnings are contextual, not prohibitions.

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The Correct AWS Use Case for UpdraftPlus

When creating an access key in IAM, AWS asks you to select a use case.

✅ Correct choice:
Application running outside AWS

This matches the reality:

• UpdraftPlus is a third-party PHP application
• It runs outside AWS
• It only needs programmatic access to S3

Selecting this option does not weaken security and does not change how credentials work.
It simply helps AWS categorize usage internally.

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Secure Architecture Overview

WordPress
 └─ UpdraftPlus
      └─ IAM User (restricted)
           └─ S3 Bucket (private)

Key principle: least privilege.

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Step 1 – Create a Dedicated S3 Bucket

Best practices:

• Private bucket (Block all public access)
• Dedicated to backups only
• Optional versioning enabled
• Optional lifecycle rules (auto-delete old backups)

Example bucket name:

my-wp-backups-prod

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Step 2 – Create a Dedicated IAM User

Never use:

• Root credentials
• A shared IAM user
• Broad policies like AmazonS3FullAccess

Create a single-purpose IAM user, for example:

updraftplus-wordpress

Programmatic access only.

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Step 3 – Attach a Minimal IAM Policy

This policy allows only what UpdraftPlus needs:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Sid": "UpdraftPlusS3Access",
      "Effect": "Allow",
      "Action": [
        "s3:PutObject",
        "s3:GetObject",
        "s3:DeleteObject",
        "s3:ListBucket"
      ],
      "Resource": [
        "arn:aws:s3:::my-wp-backups-prod",
        "arn:aws:s3:::my-wp-backups-prod/*"
      ]
    }
  ]
}

This prevents:

• Access to other buckets
• Account-wide damage if credentials leak

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Step 4 – Generate Access Keys

Generate:

• Access Key ID
• Secret Access Key

Store them securely and never commit them to Git.

Add a description such as: UpdraftPlus – production backups

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Step 5 – Configure UpdraftPlus

In WordPress:

1. Settings → UpdraftPlus → Settings
2. Select Amazon S3
3. Enter:
   • Access Key ID
   • Secret Access Key
   • Bucket name
   • Region
4. Set a bucket subpath (recommended):

wordpress/site-prod/

5. Save and test the connection

If it fails, the cause is almost always:

• Wrong region
• Incorrect IAM policy
• Typo in bucket name

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Optional Hardening (Recommended)

Lifecycle Rules

Automatically delete old backups:

• Daily: keep 14 days
• Monthly: keep 6 months

This avoids silent storage cost growth.

Server-Side Encryption

Enable default SSE-S3 (AES-256).
No code changes required.

IP Restriction (Advanced)

If your hosting provider has a static IP, you can restrict IAM access to that IP range.

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Common Mistakes to Avoid

• Using root access keys
• Granting AmazonS3FullAccess
• Making the bucket public
• Skipping lifecycle rules
• Reusing credentials across multiple sites

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Final Verdict

For WordPress + UpdraftPlus:

• Long-term access keys are normal
• AWS warnings are generic
• Least-privilege IAM policies are what actually matter

Used correctly, this setup is secure, stable, and industry-standard.

February 10, 2026

Aliexpress : The Bundle Deal Problem

Tired of AliExpress forcing you into “Bundle Deals” when you just want to buy a single item? This free userscript intercepts those sneaky links and redirects you straight to the actual product page.

The Bundle Deal Problem

AliExpress has been increasingly pushing shoppers toward “Bundle Deals” — grouped offers that look attractive but prevent you from purchasing a single item at its regular price. When you click on a product from search results, you land on an intermediate page that pressures you into adding more items to your cart.

Here’s what a bundle link looks like:

aliexpress.com/ssr/300000512/BundleDeals2?productIds=1005008154245742...

Instead of the direct product link:

aliexpress.com/item/1005008154245742.html

This dark pattern wastes your time and tricks you into spending more than you intended. Let’s fix that.

The Solution: AliExpress Bundle Bypass

This userscript automatically detects Bundle Deal links and converts them into direct product page links. No more hunting for workarounds — the script handles everything silently in the background.

Features

• Instant redirect — If you land on a Bundle page, you’re immediately redirected to the actual product
• Link rewriting — Bundle links in search results are converted in real-time as the page loads
• Click interception — Even if a link wasn’t converted, clicks are caught and redirected
• Debug panel — A visual overlay shows how many links were detected and fixed
• Visual indicators — Converted links get a green outline for confirmation

Installation Guide

The script works with any userscript manager extension. Follow the instructions for your browser below.

🟡 Google Chrome

1. Install Tampermonkey from the Chrome Web Store
2. Click the Tampermonkey icon in your browser toolbar
3. Select “Create a new script…”
4. Delete the default template code
5. Paste the script code (see below)
6. Press Ctrl+S (or Cmd+S on Mac) to save
7. Refresh AliExpress — the debug panel should appear in the top-right corner

🟠 Mozilla Firefox

1. Install Violentmonkey or Greasemonkey from Firefox Add-ons
2. Click the extension icon in your toolbar
3. Click the “+” button to create a new script
4. Paste the script code
5. Save with Ctrl+S
6. Navigate to AliExpress and test it out

🔵 Microsoft Edge

1. Install Tampermonkey for Edge from the Microsoft Edge Add-ons store
2. Click the Tampermonkey icon
3. Select “Create a new script…”
4. Paste the code and save

🦁 Brave Browser

Brave is Chromium-based, so the process is identical to Chrome. Install Tampermonkey from the Chrome Web Store — it’s fully compatible with Brave.

🍎 Safari (macOS)

Safari requires a paid extension for userscripts. Your options are:

• Userscripts (free, open-source)
• Tampermonkey for Safari (paid)

The Script

Copy the entire code below and paste it into your userscript manager:

// ==UserScript==
// @name         AliExpress Bundle Bypass
// @namespace    https://github.com/user/aliexpress-bundle-bypass
// @version      3.1.0
// @description  Bypass bundle deals and go directly to product pages
// @author       Community
// @match        *://*.aliexpress.com/*
// @match        *://*.aliexpress.ru/*
// @match        *://*.aliexpress.us/*
// @grant        GM_addStyle
// @run-at       document-end
// @license      MIT
// ==/UserScript==

(function() {
    'use strict';

    const processedLinks = new WeakSet();
    let isScanning = false;
    let scanTimeout = null;
    let scannedCount = 0;
    let foundCount = 0;
    let convertedCount = 0;

    // Debug panel
    function createDebugPanel() {
        const panel = document.createElement('div');
        panel.id = 'bundle-bypass-debug';
        panel.innerHTML = `
            <div style="
                position: fixed;
                top: 10px;
                right: 10px;
                background: #1a1a2e;
                color: #0f0;
                font-family: monospace;
                font-size: 12px;
                padding: 10px 15px;
                border-radius: 8px;
                z-index: 999999;
                max-width: 350px;
                box-shadow: 0 4px 20px rgba(0,255,0,0.3);
                border: 1px solid #0f0;
            ">
                <div style="font-weight: bold; margin-bottom: 8px; color: #0ff;">
                    ⚡ BUNDLE BYPASS v3.1
                </div>
                <div id="bb-status">Active ✓</div>
                <div id="bb-stats" style="margin-top: 8px;">
                    Scanned: <span id="bb-scanned">0</span> |
                    Bundles: <span id="bb-found">0</span> |
                    Fixed: <span id="bb-converted">0</span>
                </div>
                <button id="bb-close" style="position: absolute; top: 5px; right: 10px; background: none; border: none; color: #f00; cursor: pointer;">✕</button>
            </div>
        `;
        return panel;
    }

    function updateStats() {
        const el1 = document.getElementById('bb-scanned');
        const el2 = document.getElementById('bb-found');
        const el3 = document.getElementById('bb-converted');
        if (el1) el1.textContent = scannedCount;
        if (el2) el2.textContent = foundCount;
        if (el3) el3.textContent = convertedCount;
    }

    // Redirect if already on a bundle page
    const currentUrl = window.location.href;
    if (currentUrl.includes('BundleDeals')) {
        const match = currentUrl.match(/productIds=(\d+)/);
        if (match) {
            window.location.replace(`https://www.aliexpress.com/item/${match[1]}.html`);
            return;
        }
    }

    function extractProductId(url) {
        let match = url.match(/productIds=(\d+)/);
        if (match) return match[1];
        match = url.match(/x_object_id[%3A:]+(\d+)/i);
        if (match) return match[1];
        return null;
    }

    function scanLinks() {
        if (isScanning) return;
        isScanning = true;

        document.querySelectorAll('a[href*="BundleDeals"]').forEach(link => {
            if (processedLinks.has(link)) return;
            processedLinks.add(link);
            scannedCount++;

            const productId = extractProductId(link.href);
            if (productId) {
                foundCount++;
                link.dataset.originalBundle = link.href;
                link.href = `https://www.aliexpress.com/item/${productId}.html`;
                link.classList.add('bb-converted');
                convertedCount++;
            }
        });

        updateStats();
        isScanning = false;
    }

    function debouncedScan() {
        if (scanTimeout) clearTimeout(scanTimeout);
        scanTimeout = setTimeout(scanLinks, 200);
    }

    // Click interceptor as fallback
    document.addEventListener('click', function(e) {
        const link = e.target.closest('a');
        if (!link || !link.href.includes('BundleDeals')) return;

        const productId = extractProductId(link.href);
        if (productId) {
            e.preventDefault();
            e.stopPropagation();
            const url = `https://www.aliexpress.com/item/${productId}.html`;
            link.target === '_blank' ? window.open(url, '_blank') : window.location.href = url;
        }
    }, true);

    // Mutation observer for dynamically loaded content
    const observer = new MutationObserver(mutations => {
        if (mutations.some(m => m.target.closest('#bundle-bypass-debug'))) return;
        debouncedScan();
    });

    // Inject CSS for visual feedback
    const style = document.createElement('style');
    style.textContent = `
        a.bb-converted { outline: 3px solid #0f0 !important; outline-offset: 2px !important; }
        a[href*="BundleDeals"]:not(.bb-converted) { outline: 3px dashed #ff0 !important; }
    `;
    document.head.appendChild(style);

    // Initialize
    function init() {
        document.body.appendChild(createDebugPanel());
        document.getElementById('bb-close').onclick = () => 
            document.getElementById('bundle-bypass-debug').remove();
        scanLinks();
        observer.observe(document.body, { childList: true, subtree: true });
        let polls = 0;
        const id = setInterval(() => { scanLinks(); if (++polls >= 5) clearInterval(id); }, 2000);
    }

    document.readyState === 'loading' 
        ? document.addEventListener('DOMContentLoaded', init) 
        : init();
})();

How to Use

Once installed, the script runs automatically on all AliExpress pages. You’ll notice:

• A debug panel in the top-right corner showing real-time statistics
• A green outline around links that have been successfully converted
• A yellow dashed outline around Bundle links that haven’t been processed yet (rare)

You can close the debug panel by clicking the red ✕ button. The script will continue running in the background.

Troubleshooting

The debug panel doesn’t appear

• Make sure the script is enabled in your userscript manager
• Hard refresh the page with Ctrl+Shift+R (or Cmd+Shift+R on Mac)
• Check that the script is set to run on aliexpress.com

Counters stay at 0

This is normal if the current page doesn’t contain any Bundle links. Try searching for a product to see the script in action — Bundle Deals typically appear in search results.

The script stopped working

AliExpress frequently updates their website. If the script breaks, open DevTools (F12), inspect a product card link, and check if they still use the BundleDeals pattern in URLs. If the format changed, the script will need to be updated.

Disabling the Debug Panel

If you find the overlay distracting after confirming the script works, you can disable it permanently:

1. Open the script in your userscript manager
2. Find the init() function near the bottom
3. Delete or comment out this line: document.body.appendChild(createDebugPanel());
4. Save the script

How It Works

The script uses three layers of protection to ensure Bundle links are bypassed:

1. Instant redirect — If you’re already on a Bundle page, it extracts the product ID and redirects immediately
2. DOM scanning — It scans all links on the page and rewrites Bundle URLs to direct product URLs
3. Click interception — As a fallback, it captures clicks on Bundle links and redirects them

A MutationObserver watches for dynamically loaded content (infinite scroll, lazy loading) and processes new links as they appear.

---

Contributing

Found a bug or want to improve the script? Feel free to fork, modify, and share. If AliExpress changes their URL patterns, the key function to update is extractProductId().

---

Tested on Chrome 120+, Firefox 121+, Edge 120+, Brave 1.61+, and Safari 17+. Last updated: January 2025. Licensed under MIT.

February 10, 2026

Artificial Intelligence and the Personal Computer: A Valid Comparison, but an Incomplete One

As artificial intelligence (AI) systems rapidly improve and spread across industries, a common argument emerges: the AI shift is comparable to the rise of the personal computer (PC) in the 1980s and 1990s. According to this view, AI represents another technological wave—initially disruptive, eventually normalized—requiring adaptation rather than concern.

While this comparison is not without merit, it has clear limitations. A closer examination reveals that, although the two transformations share similarities, the nature and implications of the AI shift may be fundamentally different.

---

1. Shared characteristics between the PC and AI revolutions

There are legitimate reasons why the comparison persists.

• General-purpose technologies: Both the PC and AI are applicable across a wide range of sectors.
• Productivity gains: Each promises efficiency improvements through automation and digitalization.
• Initial anxiety: Both sparked fears about job losses and skill obsolescence.
• Learning curve: Adoption in both cases requires new competencies and changes in workflows.

From this perspective, viewing AI as part of a recurring historical pattern is understandable.

---

2. Passive tools versus active systems

A key difference lies in the nature of the technology itself.

• The personal computer is fundamentally a passive tool. It executes explicit instructions provided by a human user.
• Modern AI systems function as active systems. They generate content, infer patterns, and can operate semi-autonomously.

This distinction reshapes the human role. While PCs extend human capabilities, AI systems can, in some cases, perform tasks independently from end to end.

---

3. The type of work affected

Earlier waves of automation primarily targeted:

• physical labor,
• repetitive administrative tasks.

AI increasingly impacts work traditionally associated with human cognition:

• writing,
• analysis,
• software development,
• translation,
• design.

The PC required human judgment to interpret and apply information. AI systems increasingly operate within that interpretive layer, raising different questions about task allocation.

---

4. Speed of change and adoption

The tempo of transformation also differs significantly.

• Personal computers spread gradually over several decades.
• AI systems evolve through rapid iteration cycles, with noticeable capability jumps in months rather than years.

This acceleration compresses the time available for workers, institutions, and education systems to adapt incrementally.

---

5. Access, infrastructure, and concentration

The PC contributed to a broad democratization of computing:

• relatively affordable hardware,
• open development ecosystems,
• decentralized innovation.

Contemporary AI relies more heavily on:

• large-scale infrastructure,
• massive datasets,
• substantial capital investment.

As a result, questions about centralization of technological and economic power play a more prominent role than they did during the PC era.

---

6. Rethinking the idea of “adaptation”

The claim that one can simply “adapt” assumes that skills, once acquired, remain valuable long enough to justify the investment.

In the context of AI:

• certain skills may become obsolete quickly,
• continuous learning becomes less stable and more fragmented.

Adaptation remains possible, but it may no longer offer the same long-term security it once did.

---

7. A helpful analogy—with limits

Comparing AI to the personal computer can help reduce panic and situate innovation within historical precedent. However, the analogy becomes insufficient when examining deeper structural effects.

The PC reshaped how people worked.
AI increasingly challenges which tasks require human involvement at all.

---

Conclusion

The comparison between artificial intelligence and the personal computer is neither entirely wrong nor fully adequate. It highlights shared dynamics of technological adoption while obscuring meaningful differences in system behavior, speed of change, and economic structure.

Rather than viewing AI as a simple repetition of past technological shifts, it may be more accurate to see it as a transformation whose long-term implications—for work, skills, and value creation—are still unfolding.