# _prisma_migrations

id: e0e41be3-fcfd-438d-8eee-f3bb73f87eb6
checksum: a74541224f785357bcc7f4b22cc70b3512570177b7a1ecb33677803979120554
migration_name: 20260115120000_add_show_recent_notes
logs:
  A migration failed to apply. New migrations cannot be applied before the error is recovered from. Read more about how to resolve migration issues in a production database: https://pris.ly/d/migrate-resolve

  Migration name: 20260115120000_add_show_recent_notes

  Database error code: 1

  Database error:
  duplicate column name: showRecentNotes

     0: sql_schema_connector::apply_migration::apply_script
             with migration_name="20260115120000_add_show_recent_notes"
               at schema-engine\connectors\sql-schema-connector\src\apply_migration.rs:106
     1: schema_core::commands::apply_migrations::Applying migration
             with migration_name="20260115120000_add_show_recent_notes"
               at schema-engine\core\src\commands\apply_migrations.rs:91
     2: schema_core::state::ApplyMigrations
               at schema-engine\core\src\state.rs:226


id: 2fedc16c-afcb-40f3-b4bf-5a1983eda64e
checksum: f40d6273af2f121f73d80afedfc9df4b90929708b7dbd9f892664b285f85de6b
migration_name: 20260413130000_add_notes_view_mode
logs:
  A migration failed to apply. New migrations cannot be applied before the error is recovered from. Read more about how to resolve migration issues in a production database: https://pris.ly/d/migrate-resolve

  Migration name: 20260413130000_add_notes_view_mode

  Database error code: 1

  Database error:
  duplicate column name: notesViewMode

     0: sql_schema_connector::apply_migration::apply_script
             with migration_name="20260413130000_add_notes_view_mode"
               at schema-engine/connectors/sql-schema-connector/src/apply_migration.rs:106
     1: schema_core::commands::apply_migrations::Applying migration
             with migration_name="20260413130000_add_notes_view_mode"
               at schema-engine/core/src/commands/apply_migrations.rs:91
     2: schema_core::state::ApplyMigrations
               at schema-engine/core/src/state.rs:226


# SystemConfig

key: OPENAI_API_KEY
value:
  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


key: mcp_key_b92d5397
value:
  {"shortId":"b92d5397","name":"N8N Automation","userId":"cmk6s0uqd0000367pfema1nlr","userName":"Sepehr","userEmail":"sepehr1151@gmail.com","keyHash":"ffe7f03cfed2fa1278cfc55327356db7f104783a991272bf118076f6713bcf9a","createdAt":"2026-04-12T06:40:48.321Z","lastUsedAt":null,"active":false,"revokedAt":"2026-04-12T06:42:44.994Z"}


key: mcp_key_8a84fd86
value:
  {"shortId":"8a84fd86","name":"Test Key","userId":"cmk6s0uqd0000367pfema1nlr","userName":"Sepehr","userEmail":"sepehr1151@gmail.com","keyHash":"6e07a04dc9b331f448b85cf6fa9bce8b3ffadefb998611d32df4a579801e459d","createdAt":"2026-04-12T06:42:45.067Z","lastUsedAt":"2026-04-12T06:45:15.201Z","active":true}


key: mcp_key_0f0fe746
value:
  {"shortId":"0f0fe746","name":"claude","userId":"cmk36y0m700005rc4v7nf8183","userName":"Admin User","userEmail":"test@example.com","keyHash":"0992b582bca7f0206c033d37f6078d7940d24b56d2cab02cae125098cb853138","createdAt":"2026-04-15T21:13:02.848Z","lastUsedAt":null,"active":true}


# Note

id: cmk5x92ww00032720w05t6vda
title: Github
content: muuri, gestion de l'animation
color: default
type: text
labels: ["technologie"]
images: []
size: large
userId: cmk36y0m700005rc4v7nf8183
notebookId: cmka4p9cg0007blfvlmyobyw3
links:
  [{"url":"https://github.com/haltu/muuri","title":"GitHub - haltu/muuri: Infinite responsive, sortable, filterable and draggable layouts","description":"Infinite responsive, sortable, filterable and draggable layouts - haltu/muuri","imageUrl":"https://opengraph.githubassets.com/b27af70e01832e4e442537e6cf77b71123d3f6f4efb2fbc092806e0d17064882/haltu/muuri","siteName":"GitHub"}]

embedding:
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id: cmk611eti000hsht4ig1f62gh
title: Guide d'Achat de Billets d'Avion pour Téhéran à l'Occasion de Nowruz
content: Acheter un billet d'avion pour téhéran pour Nowruz
color: default
type: text
labels: ["Tehran","Nowruz","billet d'avion"]
images: []
size: large
userId: cmk36y0m700005rc4v7nf8183
notebookId: cmka0k7700007ehnhblkk06n6
links:
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title: Bons plans, vols et séjours dernière minute
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  https://www.lemonde.fr/international/article/2026/04/13/avec-le-blocus-annonce-du-detroit-d-ormuz-le-regime-iranien-se-prepare-a-une-epreuve-de-force-dans-la-duree_6679688_3210.html
  L’annonce avait sans doute été pensée à Washington comme une tenaille censée se refermer sur l’Iran. Elle prend, depuis dimanche 12 avril, les allures d’une escalade vertigineuse à même de plonger le Moyen-Orient dans un brasier incontrôlable. Le blocus naval, décrété par Donald Trump dans le détroit d’Ormuz, quelques heures après l’échec des négociations d’Islamabad, au Pakistan, a laissé de marbre le pouvoir iranien et son bras armé, les gardiens de la révolution, dont le commandement naval a menacé d’entraîner leurs ennemis dans un « tourbillon mortel ».

  Lire aussi | Article réservé à nos abonnés Le pari risqué de Donald Trump, qui annonce un blocus maritime de l’Iran après l’échec des premières négociations

  La levée du blocage de cet étroit couloir maritime, orchestré par l’Iran peu après le déclenchement, fin février, de l’opération militaire lancée par les Etats-Unis avec leur allié israélien, était l’un des enjeux de ces discussions, avec l’encadrement du programme nucléaire iranien. Au terme de vingt heures de pourparlers, permis par le cessez-le-feu décidé quatre jours plus tôt, l’administration Trump n’aura obtenu ni l’un ni l’autre.

  Après quatre semaines de guerre, le régime iranien, affaibli mais résilient, se pense en vainqueur. Pour le convaincre de rétablir le trafic maritime dans le détroit d’Ormuz et d’accepter des concessions sur le programme nucléaire, les experts estiment qu’il aurait fallu à la délégation américaine, composée du vice-président J. D. Vance et des deux négociateurs à tout faire de Donald Trump, Steve Witkoff et Jared Kushner, de la patience, de l’habileté et des arguments. Mais J. D. Vance n’a offert à ses interlocuteurs iraniens

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id: cmk6nyxak0001uaf3bs70qwgh
title: Introduction aux Techniques de Mise en Forme Markdown
color: default
type: text
labels: ["bug_detection"]
images: []
links: []
size: small
userId: cmk36y0m700005rc4v7nf8183
notebookId: cmkk2s31i0005wfmx0vk39lad
content:
  # 1. Titre Principal (H1)
  Ceci est un paragraphe d'introduction avec du **gras**, de l'*italique*, et du ***gras italique***.
  On peut également ~~barrer du texte~~ ou utiliser du `code en ligne` pour des termes techniques.

  ## 2. Structures de Listes (H2)

  ### Liste Ordonnée et Imbriquée
  1. Premier élément principal
  2. Deuxième élément principal
      * Sous-élément A (liste à puces)
      * Sous-élément B
          - Niveau 3 (tiret)
          - Niveau 3 bis
      * Retour au niveau 2
  3. Troisième élément

  ### Liste de Tâches (Task List)
  - [x] Tâche terminée
  - [ ] Tâche à faire
  - [ ] **Tâche urgente**

  ---

  ## 3. Tableaux et Alignement

  | Fonctionnalité | Description | Statut | Coût |
  | :--- | :---: | ---: | :--- |
  | Alignement | Gauche/Centre/Droite | Ok | 0 € |
  | **Complexité** | Haute | *En cours* | 500 € |
  | Rendu | Rapide | Fini | 10 € |

  ---

  ## 4. Blocs de Code (Syntax Highlighting)

  Voici un exemple de fonction en Python :

  ```python
  def calculer_tokens(texte):
      # Ceci est un commentaire
      mots = texte.split()
      return len(mots) * 1.3  # Estimation

  print("Test de rendu")
  ```

  Et un exemple en JSON :

  ```json
  {
    "id": 123,
    "test_mode": true,
    "tags": ["markdown", "cli", "gemini"]
  }
  ```

  ## 5. Citations et Mathématiques

  > Ceci est un bloc de citation standard.
  > Il peut s'étendre sur plusieurs lignes.
  >
  > > Ceci est une citation imbriquée (niveau 2).
  > > Utile pour les réponses de mails.

  ### Formules (LaTeX)
  Si votre CLI supporte le rendu mathématique :
  $$E = mc^2$$

  ## 6. Liens et Images

  Pour plus d'infos, voir [La documentation Markdown](https://www.markdownguide.org).

  ![Texte alternatif de l'image](http://googleusercontent.com/image_collection/image_retrieval/15624899646077402940_0 "Titre de l'image au survol")

embedding:
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id: cmk6yd8cq0001iazcz8urj7ib
title: L'Impact des Modèles de Langage en IA de 2023 à 2025
color: default
type: text
labels: []
images: []
links: []
reminder: 2026-04-15T10:00:00.000Z
size: small
userId: cmk6s0uqd0000367pfema1nlr
content:
  # L'Ère de l'IA Générative : Bilan 2023-2025

  L'année **2023** a marqué un tournant décisif avec la démocratisation des *Large Language Models* (LLM). Ce document synthétise les avancées majeures, des architectures "Mixture of Experts" à l'avènement des agents autonomes.

  > "L'IA sera probablement la technologie la plus importante sur laquelle l'humanité n'ait jamais travaillé."
  > — *Sundar Pichai*

  ---

  ## 1. Comparatif des Modèles Majeurs

  La course à la performance s'est accélérée avec l'augmentation des fenêtres de contexte.

  | Modèle | Développeur | Date de sortie | Fenêtre de Contexte | Type |
  | :--- | :---: | :---: | ---: | :--- |
  | **GPT-4** | OpenAI | Mars 2023 | 8k - 32k | Dense |
  | **Gemini 1.5 Pro** | Google | Février 2024 | **1M - 2M** | MoE (Multimodal) |
  | **Claude 3.5 Sonnet** | Anthropic | Juin 2024 | 200k | Dense |
  | **Llama 3** | Meta | Avril 2024 | 8k - 128k | Open Weights |

  ## 2. Évolution Technique : L'Attention

  Le cœur de ces systèmes reste le mécanisme d'attention, introduit dans le papier *Attention Is All You Need*[^1].

  ### Formule Mathématique
  L'attention scalaire (Scaled Dot-Product Attention) se définit ainsi :

  $$
  Attention(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V
  $$

  ### Implémentation Simplifiée (Python/PyTorch)
  Voici comment cela se traduit en code pour un ingénieur ML :

  ```python
  import torch
  import torch.nn.functional as F

  def scaled_dot_product_attention(query, key, value):
      d_k = query.size(-1)
      # Calcul des scores
      scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)
      # Application du Softmax
      p_attn = F.softmax(scores, dim=-1)
      # Multiplication avec les valeurs
      return torch.matmul(p_attn, value), p_attn
  ```

  ## 3. Les Défis Actuels (Checklist)

  Malgré les succès, plusieurs obstacles persistent pour une adoption critique en entreprise :

  - [x] **Hallucinations** : Réduites via le RAG (*Retrieval Augmented Generation*).
  - [x] **Latence** : Améliorée par des modèles "Flash" ou "Turbo".
  - [ ] **Coût énergétique** : L'inférence reste très coûteuse en GPU.
  - [ ] **Raisonnement complexe** : Le système 2 (réflexion lente) est encore expérimental.

  ## 4. Architecture Visuelle

  L'architecture Transformer, qui permet cette parallélisation, ressemble schématiquement à ceci :

  ![Architecture Transformer Simplifiée](https://upload.wikimedia.org/wikipedia/commons/thumb/9/91/Full_GPT_architecture.png/640px-Full_GPT_architecture.png "Schéma de l'architecture GPT")

  ## 5. Exemple de Réponse JSON Structurée

  De plus en plus, les LLM sont utilisés pour générer de la donnée structurée plutôt que du texte libre.

  ```json
  {
    "analyse_sentiment": {
      "score": 0.98,
      "label": "POSITIF",
      "entites_detectees": ["OpenAI", "Google", "Meta"]
    },
    "metadata": {
      "model_version": "gemini-1.5-flash",
      "timestamp": "2025-01-09T14:30:00Z"
    }
  }
  ```

  ---
  *[^1]: Vaswani et al., 2017, Google Brain.*

embedding:
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id: cmk7cxxby0001s2c3zhmpin69
title: Understanding Mathematical Notation: Fractions, Roots, and Sums
color: default
type: text
labels: []
images: []
links: []
size: small
userId: cmk6s0uqd0000367pfema1nlr
content:
   - Fractions : $\frac{a}{b}$ → $\frac{a}{b}$
    - Indices : $x_i^2$ → $x_i^2$
    - Racines : $\sqrt{x}$ → $\sqrt{x}$
    - Symboles grecs : $\alpha, \beta, \theta$ → $\alpha, \beta, \theta$
    - Sommes : $\sum_{i=1}^{n} x_i$ → $\sum_{i=1}^{n} x_i$
    - Intégrales : $\int_{a}^{b} f(x) dx$ → $\int_{a}^{b} f(x) dx$

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title: OPENAI API KEY
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