llm-ai
26
prompt-engineering-suite
Comprehensive prompt engineering with Chain-of-Thought, few-shot learning, prompt versioning, and optimization. Use when designing prompts, improving accuracy, managing prompt lifecycle.
yonatangross
data-ai
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llm-ai
26
context-engineering
Use when designing agent system prompts, optimizing RAG retrieval, or when context is too expensive or slow. Reduces tokens while maintaining quality through strategic positioning and attention-aware design.
yonatangross
data-ai
llm-ai
26
agent-loops
Agentic workflow patterns for autonomous LLM reasoning. Use when building ReAct agents, implementing reasoning loops, or creating LLMs that plan and execute multi-step tasks.
yonatangross
data-ai
llm-ai
26
golden-dataset-curation
Use when creating or improving golden datasets for AI evaluation. Defines quality criteria, curation workflows, and multi-agent analysis patterns for test data.
yonatangross
data-ai
machine-learning
26
ollama-local
Local LLM inference with Ollama. Use when setting up local models for development, CI pipelines, or cost reduction. Covers model selection, LangChain integration, and performance tuning.
yonatangross
data-ai
llm-ai
26
cache-cost-tracking
LLM cost tracking with Langfuse for cached responses. Use when monitoring cache effectiveness, tracking cost savings, or attributing costs to agents in multi-agent systems.
yonatangross
data-ai
llm-ai
26
mem0-memory
Long-term semantic memory across sessions using Mem0. Use when you need to remember, recall, or forget information across sessions, or when referencing what we discussed last time or in a previous session.
yonatangross
data-ai
llm-ai
26
langgraph-parallel
LangGraph parallel execution patterns. Use when implementing fan-out/fan-in workflows, map-reduce over tasks, or running independent agents concurrently.
yonatangross
data-ai
llm-ai
26
pgvector-search
Production hybrid search combining PGVector HNSW with BM25 using Reciprocal Rank Fusion. Use when implementing hybrid search, semantic + keyword retrieval, vector search optimization, metadata filtering, or choosing between HNSW and IVFFlat indexes.
yonatangross
data-ai
llm-ai
26
function-calling
LLM function calling and tool use patterns. Use when enabling LLMs to call external tools, defining tool schemas, implementing tool execution loops, or getting structured output from LLMs.
yonatangross
data-ai
llm-ai
26
load-context
Auto-load relevant memories at session start from both mem0 and graph
yonatangross
data-ai
llm-ai
26
ai-proofreading
็ณป็ปๅ้ไฝAIๆฃๆต็่ณ30%ไปฅไธ๏ผ้่ฟไธ้ๅฎกๆ กๆต็จ๏ผๅ ๅฎนใ้ฃๆ ผใ็ป่๏ผๅขๅ ไบบๅณใๅฝ็จๆทๆๅฐ"AIๅณๅคช้"ใ"ๅAIๅ็"ใ"้ไฝAIๆฃๆต็"ใ"ๆดๅไบบๅ็"ใ"่ช็ถไธไบ"ใ"ๅฃ่ฏญๅ"ๆถไฝฟ็จๆญคๆ่ฝใ
alchaincyf
data-ai
machine-learning
26
huggingface-transformers
Hugging Face Transformers best practices including model loading, tokenization, fine-tuning workflows, and inference optimization. Use when working with transformer models, fine-tuning LLMs, implementing NLP tasks, or optimizing transformer inference.
applied-artificial-intelligence
data-ai
llm-ai
26
add-golden
Curate and add documents to the golden dataset with multi-agent validation. Use when adding test data, creating golden datasets, saving examples.
yonatangross
data-ai
llm-ai
26
post-game-press-conference-simulator
Generate realistic coach/player interview responses for wins, losses, controversies, injuries. Authentic coachspeak and player personalities.
OneWave-AI
data-ai
llm-ai
26
prompt-caching
Provider-native prompt caching for Claude and OpenAI. Use when optimizing LLM costs with cache breakpoints, caching system prompts, or reducing token costs for repeated prefixes.
yonatangross
data-ai
llm-ai
26
context-compression
Use when conversation context is too long, hitting token limits, or responses are degrading. Compresses history while preserving critical information using anchored summarization and probe-based validation.
yonatangross
data-ai
llm-ai
26
langfuse-observability
LLM observability platform for tracing, evaluation, prompt management, and cost tracking. Use when setting up Langfuse, monitoring LLM costs, tracking token usage, or implementing prompt versioning.
yonatangross
data-ai
machine-learning
26
fine-tuning-customization
LLM fine-tuning with LoRA, QLoRA, DPO alignment, and synthetic data generation. Efficient training, preference learning, data creation. Use when customizing models for specific domains.
yonatangross
data-ai
llm-ai
26
prompt-classifier
่ชๅจ่ฏๅซprompt็ฑปๅๅนถไฟๅญๅฐ็ธๅบๅ็ฑป๏ผๆๆฏ/ๅ ๅฎน/ๆๅญฆ/ไบงๅ/้็จ๏ผ๏ผๆฏๆ่ชๅจๆไปถๅฝๅๅ็ดขๅผ็ฎก็ใๅฝ็จๆทๆๅฐ"ไฟๅญprompt"ใ"่ฎฐๅฝprompt"ใ"็ฎก็prompt"ใ"ๆด็prompt"ใ"promptๅบ"ๆถไฝฟ็จๆญคๆ่ฝใ
alchaincyf
data-ai
llm-ai
26
langgraph-supervisor
LangGraph supervisor-worker pattern. Use when building central coordinator agents that route to specialized workers, implementing round-robin or priority-based agent dispatch.
yonatangross
data-ai
llm-ai
26
llm-streaming
LLM streaming response patterns. Use when implementing real-time token streaming, Server-Sent Events for AI responses, or streaming with tool calls.
yonatangross
data-ai