tokenize("Hello world")
[15496, 995]
embedding_layer(tokens)
attention_weights = softmax(Q@K.T)
for layer in range(12):
  x = transformer_block(x)
  x = layer_norm(x)
logits = linear_projection(x)
next_token = argmax(logits)
if next_token == EOS: break
W = W - lr * gradient
loss = cross_entropy(pred, target)
multi_head_attention()
Q = x @ W_q
K = x @ W_k
V = x @ W_v
attention = Q @ K.T / sqrt(d_k)
attention = softmax(attention)
output = attention @ V
feedforward_network(x)
x = relu(x @ W1 + b1)
x = x @ W2 + b2
return x + residual
position_encoding(seq_len)
for i in range(max_length):
  logits = model(input_ids)
  probs = softmax(logits / temp)
  next_id = sample(probs)
  input_ids.append(next_id)
  if next_id == stop_token:
    break
kv_cache[layer] = (k, v)
batch_size = 32
hidden_dim = 768
num_heads = 12
vocab_size = 50257
backpropagation()
grad_output = loss.backward()
for param in model.parameters():
  param.grad = compute_grad(param)
  param.data -= lr * param.grad
optimizer.step()
optimizer.zero_grad()
torch.cuda.empty_cache()
memory_usage = get_memory()
if memory_usage > threshold:
  gradient_checkpointing = True
learning_rate = 1e-4
input_text = "The future of AI"
tokens = tokenizer.encode(input_text)
embeddings = embed_tokens(tokens)
pos_embeddings = positional_encoding
x = embeddings + pos_embeddings
for transformer_layer in model:
  x = transformer_layer(x)
output_logits = x @ embedding_matrix.T
predictions = softmax(output_logits)
generated_text = decode(predictions)
temperature = 0.7
top_p = 0.9
class TransformerBlock:
  def __init__(self, d_model, n_heads):
    self.attention = MultiHeadAttention()
    self.feedforward = FeedForward()
    self.norm1 = LayerNorm(d_model)
    self.norm2 = LayerNorm(d_model)
  def forward(self, x):
    x = x + self.attention(self.norm1(x))
    x = x + self.feedforward(self.norm2(x))
    return x
model.train()
def generate_text(prompt, max_tokens=100):
  tokens = tokenize(prompt)
  for i in range(max_tokens):
    logits = model(tokens)
    next_token = sample_from_logits(logits)
    tokens.append(next_token)
    if next_token == EOS_TOKEN:
      break
  return detokenize(tokens)
CUDA_VISIBLE_DEVICES=0
mixed_precision = True
gradient_accumulation_steps = 4
attention_matrix = torch.zeros(seq_len, seq_len)
for head in range(num_heads):
  q = query_projection(x)
  k = key_projection(x)
  v = value_projection(x)
  scores = q @ k.transpose(-2, -1)
  scores = scores / math.sqrt(head_dim)
  attn_weights = F.softmax(scores, dim=-1)
  output = attn_weights @ v
concat_outputs = torch.cat(head_outputs)
final_output = output_projection(concat_outputs)
dropout_rate = 0.1
model_parameters = 175_000_000_000
context_length = 2048
vocabulary_size = 50257
embedding_dimension = 12288
number_of_layers = 96
number_of_heads = 96
training_data_size = "45TB"
computational_cost = "3.14e23 FLOPs"
inference_time = "~100ms"
memory_requirement = "350GB"
if torch.cuda.is_available():
  device = "cuda"
model.to(device)

Welcome to SIA Copper Line - Official Website

We develop intelligent agent-based solutions that automate routine business operations and implement management control tools for small enterprises.

Our focus is on affordable automation through AI-based agents, daily performance monitoring, and real-time business alerts that keep you informed of your business health at all times.

AI Automation Business Intelligence Real-time Monitoring

Our platform integrates with Airtable to track daily operational data compared to monthly plans. We generate visual dashboards and send automated alerts via WhatsApp for early detection of stock issues, sales drops, or production failures.

The entire logic is built with n8n, enabling end-to-end data automation. Business owners can remotely control operations through clear daily metrics.

Our founder holds a Ph.D. in Economics focused on automated production management systems, and an EMBA from Stockholm School of Economics with a thesis on Automation of managerial control for quick-service restaurant chains.

Read our LinkedIn articles:

Cheops Pyramid
Cheops Pyramid as the Ultimate Achievement of Humanity
ChatWise App
ChatWiseApp – Your Universal Assistant for Working with AI
Emotion Speach Synthesis
Revolution in Emotion Synthesis: GPT-4o, Mini TTS, OpenAI
How Many Kilowatt Worker Cost?
How Many Kilowatt-Hours Does One Worker Cost?
Manus.im
How Chinese Startups Are Disrupting Stock Prices in Silicon Valley
Structuring Data for LLM
Structuring Data for OpenAI Models: Key Concepts and Insights
Libraries of the Future
Libraries of the Future: How LLMs Transforms Books
Intellectual Convergence
Intellectual Convergence: How AI Tools Can Lead to Loss of Perspective
Future Hardware for Neural Networks
Future Hardware for Neural Networks: Parallels with Bitcoin Mining
Future Work
The Future of Work: How AI Is Revolutionizing Intellectual Tasks
LinkedBots
LinkedBots: Hiring AI Assistants Instead of People
True 3D Movie
Have You Ever Really Seen a True 3D Movie?
DeepSeek: A New Challenge for the U.S.
DeepSeek: A New Challenge for the U.S.
The Role of AI in Preserving Endangered Languages.
The Role of AI in Preserving and Reviving Endangered Languages.
Google I/O 2025.
Google I/O 2025.
AI: From Translation Engines to Intelligent Agents.
AI: From Translation Engines to Intelligent Agents.
Vector Search as the Key to Full-Fledged LLM Agents.
Vector Search as the Key to Full-Fledged LLM Agents.
Mary Meeker on AI: The 'Queen of Trends' Points the Way to a New Era
Mary Meeker on AI: The "Queen of Trends" Points the Way to a New Era
True Artificial Intelligence vs. Modern Models: Apple's Perspective.
True Artificial Intelligence vs. Modern Models: Apple's Perspective.
The Future of Interfaces: From Typing Commands to Silent Thought.
The Future of Interfaces: From Typing Commands to Silent Thought.
Neural Networks: Why This Metaphor Can Be Misleading
"Neural Networks" — Why This Metaphor Can Be Misleading
Mac M4 + DeepSeek.R1.0528.Qwen3.8B: Next-Level Local AI Clouds
Mac M4 + DeepSeek.R1.0528.Qwen3.8B: Next-Level Local AI Clouds

Privacy Policy

Last updated: 20.04.2025

This Privacy Policy describes how SIA Copper Line collects, uses, and shares information when you use our services, including our WhatsApp Business integration.

Information We Collect

We may collect the following information:

How We Use Your Information

Data Sharing

We may share your information with:

We do not sell your personal information to third parties.

Data Security

We implement appropriate technical and organizational measures to protect your personal data against unauthorized access, alteration, disclosure, or destruction.

Data Retention

We retain your data only as long as necessary to provide the service or as required by law.

Your Rights

Under the GDPR, you have the right to:

To exercise these rights, please contact us at info@copperline.info.

Contact

If you have questions about this Privacy Policy, please email us at info@copperline.info.

Terms of Service

Last updated: 20.04.2025

By using SIA Copper Line services, you agree to the following terms:

1. Service Description

SIA Copper Line provides intelligent agent-based solutions that automate routine business operations and implement management control tools for small enterprises. Our services include integration with Airtable and WhatsApp for business monitoring and alerts.

2. User Responsibilities

You agree to:

3. Intellectual Property

All content and technology provided by SIA Copper Line are protected by intellectual property laws. You may not copy, modify, or distribute our technology without permission.

4. Limitation of Liability

SIA Copper Line shall not be liable for any indirect, incidental, or consequential damages arising from the use of our services.

Contact Us

SIA COPPER LINE

VAT number EU: LV50203082681

LEI number: 2549002OO19GM8GJZG60

Email: info@copperline.info

Phone: +(371) 22030215

Address: Latvia, Jurmala, Rigas iela 49-28