# _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


# SystemConfig

key: OPENAI_API_KEY
value:
  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


# Note

id: cmk5x92ww00032720w05t6vda
title: Github
content: muuri, gestion de l'animation
color: default
type: text
labels: []
images: []
size: small
userId: cmk36y0m700005rc4v7nf8183
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: small
userId: cmk36y0m700005rc4v7nf8183
links:
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id: cmk6nyxak0001uaf3bs70qwgh
title: Introduction aux Techniques de Mise en Forme Markdown
color: default
type: text
labels: []
images: []
links: []
size: small
userId: cmk36y0m700005rc4v7nf8183
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
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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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id: cmk7ipz4d0007s2c303ywgbgp
title: Feature Comparison of Note-Taking Apps
color: default
type: text
labels: []
images: []
links: []
size: large
userId: cmk36y0m700005rc4v7nf8183
content:
  Competitive Positioning

    | Feature            | Google Keep | Notion         | Memento Phase 1 |
    |--------------------|-------------|----------------|-----------------|
    | Fast Capture       | ✅ Yes      | ❌ No          | ✅ Yes          |
    | Integrated AI      | ❌ No       | ⚠️ Paid/add-on | ✅ Yes, native  |
    | Semantic Search    | ❌ No       | ⚠️ Partial     | ✅ Yes, hybrid  |
    | Proactive Insights | ❌ No       | ❌ No          | ✅ EXCLUSIVE    |
    | Zero-Config        | ✅ Yes      | ❌ No          | ✅ Yes          |
    | Privacy (Local)    | ❌ No       | ❌ No          | ✅ EXCLUSIVE    |
    | Multilingual       | ⚠️ Limited  | ⚠️ Limited     | ✅ Yes, native  |

embedding:
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id: cmk83qfr6000ds2c3a8ytanwq
title: IA
color: default
type: text
labels: []
images: []
size: small
userId: cmk70e4w80000109qqgedk1f0
content:
  # The Era of Generative AI: 2023-2025 Review

  The year **2023** marked a decisive turning point with the democratization of *Large Language Models* (LLM). This document synthesizes major advances, from "Mixture of Experts" architectures to the advent of autonomous agents.

  > "AI is probably the most important thing humanity has ever worked on."
  > — *Sundar Pichai*

  ---

  ## 1. Major Model Comparison

  The race for performance accelerated with the increase in context windows.

  | Model | Developer | Release Date | Context Window | Type |
  | :--- | :---: | :---: | ---: | :--- |
  | **GPT-4** | OpenAI | March 2023 | 8k - 32k | Dense |
  | **Gemini 1.5 Pro** | Google | February 2024 | **1M - 2M** | MoE (Multimodal) |
  | **Claude 3.5 Sonnet** | Anthropic | June 2024 | 200k | Dense |
  | **Llama 3** | Meta | April 2024 | 8k - 128k | Open Weights |

  ## 2. Technical Evolution: Attention

  The core of these systems remains the attention mechanism, introduced in the paper *Attention Is All You Need*[^1].

  ### Mathematical Formula
  Scaled Dot-Product Attention is defined as follows:

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

  ### Simplified Implementation (Python/PyTorch)
  Here is how this translates into code for an ML engineer:

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

  def scaled_dot_product_attention(query, key, value):
      d_k = query.size(-1)
      # Calculate scores
      scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)
      # Apply Softmax
      p_attn = F.softmax(scores, dim=-1)
      # Multiply by values
      return torch.matmul(p_attn, value), p_attn
  ```

  ## 3. Current Challenges (Checklist)

  Despite successes, several obstacles persist for critical enterprise adoption:

  - [x] **Hallucinations**: Reduced via RAG (*Retrieval Augmented Generation*).
  - [x] **Latency**: Improved by "Flash" or "Turbo" models.
  - [ ] **Energy Cost**: Inference remains very GPU-expensive.
  - [ ] **Complex Reasoning**: System 2 (slow thinking) is still experimental.

  ## 4. Visual Architecture

  The Transformer architecture, which allows this parallelization, looks schematically like this:

  ![Simplified Transformer Architecture](https://upload.wikimedia.org/wikipedia/commons/thumb/9/91/Full_GPT_architecture.png/640px-Full_GPT_architecture.png "GPT Architecture Diagram")

  ## 5. Structured JSON Response Example

  Increasingly, LLMs are used to generate structured data rather than free text.

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

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

links:
  [{"url":"https://www.openai.com","title":"OpenAI","description":"We believe our research will eventually lead to artificial general intelligence, a system that can solve human-level problems. Building safe and beneficial AGI is our mission.","imageUrl":"https://images.ctfassets.net/kftzwdyauwt9/3KGOHkSXu53naMuSFNaiwv/cdb0e2f899f524abb71314ab20e09c9c/OAI-white-on-black.png?w=1600&h=900&fit=fill","siteName":""}]

embedding:
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id: cmk83u28p000js2c3mfipbtmy
title: Création d'une carte interactive des notes sur Sketch
color: default
type: text
labels: ["organisation","carte mentale"]
images: []
links: []
size: small
userId: cmk36y0m700005rc4v7nf8183
content:
  Je pense une idée qu'on pourrait mettre également en place, c'est de créer une carte ou une arme, un arbre à partir de. De les notes qui sont sur Sketch. Et en plus Alors qu'on pourrait également avoir aussi un système automatique 

embedding:
  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id: cmk8c4cr2000rs2c3fs8np1o4
title: نوستالژی انقلاب ۵۷ و چالش‌های امروز
color: default
type: text
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size: small
userId: cmk36y0m700005rc4v7nf8183
content:
  هنوز خاطره جمعیِ ملت در نوستالژی ۵۷ مانده است و می‌پندارد که با چند هیاهوی خیابانی، پیشوای مخالفان سوارِ طیّاره گردد و به طهران آید و بساط دولت نو برچیند!

  غافل از آن‌که زمانه دگرگون شده و چه بسا در این آشوب، نه دولت نو، که اساس مُلک و تمامیت ارض نیز به مخاطره افتد.

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id: cmk8csoun0011s2c33967pou9
title: Problèmes de saisie dans la barre de recherche
color: default
type: text
labels: ["barre de recherche","saisie de texte"]
images: []
links: []
size: medium
userId: cmk36y0m700005rc4v7nf8183
content:
  Il est également évident qu'il y a un problème dans la barre de recherche lorsque l'on tente d'insérer quelque chose ; soit le texte s'efface spontanément, soit on ne parvient pas à continuer la saisie, soit encore le curseur se déplace de manière inattendue.

embedding:
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id: cmk8qcziy0001ak4bv4ui6mgi
title: Défis Actuels de l'IA en Entreprise
color: default
type: text
size: small
userId: cmk6s0uqd0000367pfema1nlr
content:
  ## 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 l

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title: React hooks
content: "React hooks patterns and performance optimization"
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title: Next.js server
content: "Next.js server components best practices"
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id: cmk8v8udj000b2p4k3kjvyh55
title: programme
content: Ce soir, je suis en train de programmer. Une application de prise de notes avec RH GS.
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type: text
size: small
userId: cmk36y0m700005rc4v7nf8183
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id: cmk8vdg67000d2p4khfd1he04
title: Application avancée de régression linéaire en développement
color: default
type: text
size: small
userId: cmk36y0m700005rc4v7nf8183
content:
  Également en train de développer une application de régression linéaire qui est vraiment très bien créée, très poussée ce soir.

embedding:
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