Machine Learning on [ MECANIK DEV ]

Fine-Tuning vs RAG vs Prompting: What Each Costs

Sun, 09 Aug 2026 19:00:00 +0100

The fine-tuning vs RAG question usually arrives as a statement: “we need to fine-tune a model on our data.” It is one of the most expensive sentences in enterprise AI, and it is usually wrong. Not always, but usually. The request nearly always means one of two things: the model does not know about our business, or the model does not answer the way we want. Fine-tuning is a poor solution to the first and an expensive solution to the second.

Self-Hosting Kimi K3: Hardware, Cost and Sovereignty

Wed, 05 Aug 2026 19:00:00 +0100

Self-hosting Kimi K3 became technically possible on 27 July 2026, when Moonshot AI published the weights for a 2.8-trillion-parameter model alongside production inference support. A great many organisations read that news and concluded they could now run frontier-class reasoning on their own hardware and stop paying per token.

That conclusion is usually wrong, but not for the reason people expect. The engineering is achievable. The arithmetic is what defeats most projects, and it defeats them quietly, several months after the budget was approved.

Retrieval-Augmented Generation (RAG) Explained 2026

Sat, 11 Jul 2026 19:00:00 +0100

A general-purpose AI model knows a lot about the world and nothing about your business. It has never seen your product manuals, your internal policies, or last quarter’s reports. Retrieval-augmented generation (RAG) is the technique that closes that gap: it lets a model answer questions using your documents, accurately and with sources, without retraining the model. This guide explains what RAG is, how it works, and when to use it.

AI Software Development - A UK Business Guide for 2026

Thu, 18 Jun 2026 07:00:00 +0100

AI software development is not a future trend for UK businesses; it is already the baseline. Search interest in the term has grown 30% in the past three months, and interest in “AI software development company” is up 60%. The businesses that understand this shift now will have a significant head start over those still deciding whether to engage.

This guide explains what AI software development actually means in practice, what it changes about how software gets built, and what UK businesses should look for when choosing a partner to help them do it.

OpenAI ChatGPT 5 vs Grok 4 - Which one creates better Python code?

Tue, 12 Aug 2025 18:10:00 +0100

When it comes to AI-assisted coding, OpenAI ChatGPT 5 and Grok 4 are two of the hottest tools of 2025. Both claim to write Python code with high accuracy and efficiency, but which one actually delivers cleaner, more maintainable scripts in real-world scenarios?

In this article, we’ll pit ChatGPT 5 and Grok 4 against each other in multiple Python challenges. We will review code correctness, readability, execution speed, and adaptability when given tricky prompts.

ChatGPT 5 Review - Features, Improvements & Real-World Performance

Fri, 08 Aug 2025 18:50:00 +0100

ChatGPT 5 has officially arrived, and it’s already making waves in the AI world. Building on the success of GPT-4, OpenAI’s latest release isn’t just about raw intelligence, it’s about speed, accuracy, and adaptability.

In this hands-on ChatGPT 5 review, I’ll walk you through my hands-on experience with ChatGPT 5, highlight the standout features, and help you decide whether it’s worth your time if you’re already using GPT-4.

What’s New in ChatGPT 5

OpenAI has tightened up the architecture, refined training methods, and added more safety guardrails - but here are the changes you’ll actually feel when you use it:

Tiny BPE Trainer – A Fast and Lightweight BPE Trainer in C++

Thu, 07 Aug 2025 20:00:00 +0100

Introducing Tiny BPE Trainer

Most modern NLP models today from GPT to RoBERTa, rely on subword tokenization using Byte Pair Encoding (BPE). But what if you want to train your own vocabulary in pure C++?

Meet Tiny BPE Trainer - a blazing-fast, header-only BPE trainer written in modern C++17/20, with zero dependencies, full UTF-8 support, and HuggingFace-compatible output (vocab.txt, merges.txt).

Why Another BPE Trainer?

Because existing options are often:

Python-only, with heavy runtime dependencies (Rust, Protobuf, etc.)
Not easily embeddable in C++ applications
Not focused on speed, simplicity, or cross-platform usage

Tiny BPE Trainer is:

A Fast, UTF-8 Aware C++ Tokenizer for NLP & ML

Wed, 06 Aug 2025 06:00:00 +0100

Introducing Modern Text Tokenizer

Modern natural language processing (NLP) models like BERT, DistilBERT, and other transformer-based architectures rely heavily on effective tokenization. But C++ developers often face limited options like bloated dependencies, poor Unicode support, or lack of compatibility with vocab-based encoders.

That’s why I created Modern Text Tokenizer - a blazing-fast, header-only C++ tokenizer that’s UTF-8 aware, zero-dependency, and ML-ready out of the box.

What Makes It Unique?

Zero Dependencies – No Boost, no ICU, no external libs.
UTF-8 Safe – Correctly handles multilingual text, emojis, and multibyte characters.
Header-Only – Drop it into your project and go.
Vocabulary Encoding – Load vocab.txt from HuggingFace and generate token IDs.
Transformer-Ready – Supports [CLS], [SEP], [PAD], and sequence formatting.

Key Features

Fast ASCII vs Unicode branching using std::string_view

Fluent API for configuration:

1TextTokenizer tokenizer;
2tokenizer
3.set_lowercase(true)
4.set_split_on_punctuation(true)
5.set_keep_punctuation(true);

Load vocabularies:
```
1tokenizer.load_vocab("vocab.txt");
```

Encode / Decode:

1auto ids = tokenizer.encode("Hello world!");
2std::string decoded = tokenizer.decode(ids);

Performance

1Performance test with 174000 characters
2
3Results:
4Tokenization: 2159 μs (22000 tokens)
5Encoding: 1900 μs
6Decoding: 430 μs
7Total time: 4.49 ms
8Throughput: 36.97 MB/s

Benchmarked on Ryzen 9 5900X @ -O3 in release mode.

Does True AI Exist? Unraveling the Myths and Reality

Sun, 27 Aug 2023 06:16:00 +0100

Artificial Intelligence (AI) has been an intriguing subject, often wrapped in an enigma, driven by speculation, and enhanced with Hollywood glitz.

While movies paint a picture of AI as sentient entities, filled with emotions and consciousness, one must wonder if today’s AI mirrors such representation. It’s time to uncover the reality of AI in the contemporary era.

Understanding the Basics of AI

To decode AI, a foundational understanding is pivotal. Artificial Intelligence fundamentally revolves around creating computer systems capable of executing tasks that traditionally mandate human intelligence. From speech recognition to decision-making, these tasks have one essence - they perform rather than genuinely understand or feel.