19 Sep 2025
Day 19 #30DaysOfCode
Google & GitHub Sign-In WORKING!
β
One-click auth with Google
β
Seamless login with GitHub
β
Real-time user state logout
Real auth. Real users. Real progress.
π: validation-yup-ebon.vercel.aβ¦
#React #Firebase #GitHubAuth #BuildInPublic #WomenInTech
2
19
116
13 Jul 2025
Day 6 of #100DaysOfCode β
π Integrated Google GitHub login via Passport.js
π οΈ Created /api/profile route (bio, skills, socials)
Building a solid auth flow for DevConnect π
#MERN #NodeJS #OAuth #GoogleAuth #GitHubAuth #ReactJS #100DaysChallenge
3
42
13 May 2025
π¨Need user login fast? Get Google & GitHub auth set up in under a minute! No code, no stress. Watch this snippet to see how itβs done π youtu.be/Lo61V-8uxrA
#abp #googlelogin #githubauth #userauthentication
2
160
27 Jul 2024
Generated from Claud with no prior context except the seed:
"Hi Claude I want to serve search results from a Github codebase using vector search over the chunks of the code base. Tell me some high level software architecture designs for this description:"
And had a couple of phases of conversation. And it managed to produce this. Very based Claude, very based
/vector-search-github/
βββ data_ingestion/
β βββ github_api_client.py
β β class GitHubAuth:
β β - token: str
β β __init__(token: str)
β β get_headers() -> dict
β β
β β class GitHubRepo:
β β - name: str
β β - api_url: str
β β __init__(name: str, api_url: str)
β β get_file_list() -> list[str]
β β
β β class GitHubClient:
β β - auth: GitHubAuth
β β __init__(auth: GitHubAuth)
β β fetch_repository(repo: GitHubRepo) -> dict
β β get_file_content(repo: GitHubRepo, file_path: str) -> str
β β get_commit_history(repo: GitHubRepo, file_path: str) -> list
β β
β βββ code_chunker.py
β β class ChunkConfig:
β β - chunk_size: int
β β - overlap: int
β β __init__(chunk_size: int, overlap: int)
β β
β β class Chunk:
β β - content: str
β β - start_line: int
β β - end_line: int
β β __init__(content: str, start_line: int, end_line: int)
β β
β β class CodeChunker:
β β - config: ChunkConfig
β β __init__(config: ChunkConfig)
β β chunk_code(code_string: str) -> list[Chunk]
β β process_file(file_content: str) -> list[Chunk]
β β
β βββ text_embedding.py
β β class EmbeddingModel:
β β - name: str
β β - dimension: int
β β __init__(name: str, dimension: int)
β β load() -> None
β β
β β class TextEmbedder:
β β - model: EmbeddingModel
β β __init__(model: EmbeddingModel)
β β embed_text(text: str) -> np.ndarray
β β embed_chunks(chunks: list[Chunk]) -> list[np.ndarray]
β β
β β class SimilarityCalculator:
β β compute_similarity(vec1: np.ndarray, vec2: np.ndarray) -> float
β β
β βββ code_analyzer.py
β β class CodeStructure:
β β - functions: list[str]
β β - classes: list[str]
β β __init__(functions: list[str], classes: list[str])
β β
β β class ComplexityMetrics:
β β - cyclomatic_complexity: int
β β - cognitive_complexity: int
β β __init__(cyclomatic_complexity: int, cognitive_complexity: int)
β β
β β class CodeAnalyzer:
β β extract_structure(code: str) -> CodeStructure
β β analyze_complexity(code: str) -> ComplexityMetrics
β β detect_language(code: str) -> str
βββ vector_database/
β βββ vector_store.py
β β class VectorEntry:
β β - id: str
β β - vector: np.ndarray
β β - metadata: dict
β β __init__(id: str, vector: np.ndarray, metadata: dict)
β β
β β class VectorStore:
β β - database: DatabaseManager
β β __init__(database: DatabaseManager)
β β insert_vector(entry: VectorEntry) -> None
β β search_similar(query_vector: np.ndarray, top_k: int) -> list[VectorEntry]
β β delete_vector(vector_id: str) -> None
β β update_vector(vector_id: str, new_entry: VectorEntry) -> None
β β
β βββ indexing.py
β β class IndexConfig:
β β - algorithm: str
β β - parameters: dict
β β __init__(algorithm: str, parameters: dict)
β β
β β class VectorIndex:
β β - config: IndexConfig
β β - index: Any # Placeholder for the actual index structure
β β __init__(config: IndexConfig)
β β build_index(vectors: list[np.ndarray]) -> None
β β query(query_vector: np.ndarray, num_results: int) -> list[int]
β β add_to_index(vector: np.ndarray, id: int) -> None
β β remove_from_index(id: int) -> None
β β
β βββ database_manager.py
β β class DatabaseConfig:
β β - url: str
β β - max_connections: int
β β __init__(url: str, max_connections: int)
β β
β β class DatabaseManager:
β β - config: DatabaseConfig
β β - connection: Any # Placeholder for the actual database connection
β β __init__(config: DatabaseConfig)
β β create_tables() -> None
β β execute_query(query: str, params: tuple) -> None
β β fetch_all(query: str, params: tuple) -> list
β β close_connection() -> None
βββ search_api/
β βββ query_handler.py
β β class Query:
β β - text: str
β β - filters: dict
β β __init__(text: str, filters: dict)
β β
β β class SearchResult:
β β - content: str
β β - similarity: float
β β - metadata: dict
β β __init__(content: str, similarity: float, metadata: dict)
β β
β β class QueryHandler:
β β - vector_store: VectorStore
β β - text_embedder: TextEmbedder
β β __init__(vector_store: VectorStore, text_embedder: TextEmbedder)
β β process_query(query: Query) -> list[SearchResult]
β β format_results(search_results: list[SearchResult]) -> list[dict]
β β
β βββ vector_converter.py
β β class VectorConverter:
β β - text_embedder: TextEmbedder
β β __init__(text_embedder: TextEmbedder)
β β text_to_vector(text: str) -> np.ndarray
β β
β β class VectorCompressor:
β β - compression_method: str
β β __init__(compression_method: str)
β β compress_vector(vector: np.ndarray) -> np.ndarray
β β decompress_vector(compressed_vector: np.ndarray) -> np.ndarray
β β
β βββ similarity_search.py
β β class SearchConfig:
β β - top_k: int
β β - similarity_threshold: float
β β __init__(top_k: int, similarity_threshold: float)
β β
β β class SimilaritySearch:
β β - vector_store: VectorStore
β β - vector_index: VectorIndex
β β - config: SearchConfig
β β __init__(vector_store: VectorStore, vector_index: VectorIndex, config: SearchConfig)
β β search(query_vector: np.ndarray) -> list[SearchResult]
β β range_search(query_vector: np.ndarray, radius: float) -> list[SearchResult]
β β
β βββ search_optimizer.py
β β class QueryCache:
β β - cache: dict
β β - max_size: int
β β __init__(max_size: int)
β β add(query: str, results: list[SearchResult]) -> None
β β get(query: str) -> list[SearchResult]
β β
β β class SearchOptimizer:
β β - similarity_search: SimilaritySearch
β β - query_cache: QueryCache
β β __init__(similarity_search: SimilaritySearch, query_cache: QueryCache)
β β optimize_query(query: str) -> str
β β cache_frequent_queries(query_history: list[str]) -> None
β β suggest_related_queries(query: str) -> list[str]
βββ web_interface/
β βββ app.py
β β class FlaskApp:
β β - app: Flask
β β - query_handler: QueryHandler
β β __init__(query_handler: QueryHandler)
β β index() -> str
β β search() -> str
β β advanced_search() -> str
β β api_search() -> dict
βββ background_jobs/
β βββ update_codebase.py
β β class UpdateTask:
β β - repo: GitHubRepo
β β - last_update: datetime
β β __init__(repo: GitHubRepo)
β β
β β class CodebaseUpdater:
β β - github_client: GitHubClient
β β - vector_store: VectorStore
β β - tasks: list[UpdateTask]
β β __init__(github_client: GitHubClient, vector_store: VectorStore)
β β check_for_updates() -> list[str]
β β process_updates(updates: list[str]) -> None
β β
β βββ refresh_vector_db.py
β β class RefreshTask:
β β - chunk: Chunk
β β - last_refresh: datetime
β β __init__(chunk: Chunk)
β β
β β class VectorDBRefresher:
β β - vector_store: VectorStore
β β - code_chunker: CodeChunker
β β - text_embedder: TextEmbedder
β β - tasks: list[RefreshTask]
β β __init__(vector_store: VectorStore, code_chunker: CodeChunker, text_embedder: TextEmbedder)
β β refresh_database() -> None
β β process_chunk(chunk: Chunk) -> None
β β
β βββ job_scheduler.py
β β class Job:
β β - id: str
β β - func: callable
β β - trigger: str
β β - kwargs: dict
β β __init__(id: str, func: callable, trigger: str, **kwargs)
β β
β β class JobScheduler:
β β - jobs: list[Job]
β β __init__()
β β add_job(job: Job) -> None
β β remove_job(job_id: str) -> None
β β start() -> None
β β shutdown() -> None
βββ config/
β βββ settings.py
β βββ logging_config.py
β β class LogConfig:
β β - level: str
β β - format: str
β β - file: str
β β __init__(level: str, format: str, file: str)
β β
β β setup_logging(config: LogConfig) -> None
β β get_logger(name: str) -> Logger
βββ utils/
β βββ performance_monitor.py
β β class Timer:
β β - start_time: float
β β - end_time: float
β β start() -> None
β β stop() -> float
β β
β β class PerformanceMonitor:
β β - timers: dict[str, Timer]
β β - memory_usage: list[float]
β β start_timer(operation: str) -> None
β β stop_timer(operation: str) -> float
β β log_memory_usage() -> None
β β generate_report() -> dict
β β
β βββ error_handler.py
β β class ErrorContext:
β β - module: str
β β - function: str
β β - parameters: dict
β β __init__(module: str, function: str, parameters: dict)
β β
β β class ErrorHandler:
β β - logger: Logger
β β __init__(logger: Logger)
β β handle_error(error: Exception, context: ErrorContext) -> None
β β log_error(error: Exception, context: ErrorContext) -> None
β β notify_admin(error: Exception, context: ErrorContext) -> None
βββ tests/
β βββ test_github_api_client.py
β βββ test_code_chunker.py
β βββ test_text_embedding.py
β βββ test_vector_store.py
β βββ test_query_handler.py
β βββ test_similarity_search.py
β βββ test_web_interface.py
βββ main.py
initialize_components() -> tuple
setup_background_jobs(job_scheduler: JobScheduler) -> None
main() -> None
1
1
71
14 Jul 2019
Crucial detail: this library isn't just for use as a Datasette plugin. If you're writing your own ASGI apps using something like @_tomchristie's excellent Starlette you can wrap your application in my GitHubAuth middleware to add GitHub authentication to it - see blog for details
1
8 Jul 2019
And because it's ASGI, you don't even need to be running Datasette to add GitHub authentication to your own project: just wrap your ASGI application in my GitHubAuth ASGI middleware class.
1
1
10
