AI
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How to reduce github copilot`s premium requests usage and maximize efficiency
How to reduce github copilots premium requests usage and maximize efficiency ? Make a plan, a kaizen plan at best. Instruct precisely, cover edge cases, allow all tools to execute and pray the LLM will understand You. Want to share my simple methodology that not only can save money but also ease in and smoothen out the workflow. RTFM ! As always You could benefit from RTFM ! Reading the foqing / friendly / flopsy manual. I know You never read it cause real man don`t do it ( how about real woman ? ) ? God knows if gamers would not have to go through the tutorial, they would…
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Deeplearning.ai text-embedding model error. Change to text-embedding-005
Deeplearning.ai text-embedding model error for “textembedding-gecko@001” requires You to simply change it to another one. Instead of the original pretrained models we should use the newer ones. Those models are succeeding the gecko series in Vertex AI. Google Vertex AI Options OpenAI Options Those are just some of the available models. Usage Comparison Model Provider Dims Max Tokens Best For text-embedding-005 Google 768 2048 English/code gemini-embedding-001 Google 3072 Varies High quality text-embedding-3-small OpenAI 1536 8191 General/RAG Summary Once upon a time, and still we can find many many things thanks to start overflow and people willing to share knowledge… let us hope the next time you google something…
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Productive struggle vs AI slop
Productive struggle vs AI slop is quite the topic these days. On one hand we have to do the hard, boring, frustrating work. Do it long enough to learn the whole lot, reshape skills, gain confidence. Opposition of escaping to quick fixes with or without AI. When used in conjuction with AI, we should still keep the struggle but without the pointless friction. That way more energy and cognitive power goes into craft and progress, long-term growth… instead of refactoring some legacy code and working over things that should not be that hard in the first place. What is productive struggle? Productive struggle is the time and space where a…
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Statics VS AI code analysis ~13 tools
Statics VS AI code analysis works best using the pros from both words. Go hybrid ! Static tools understand the syntext, hardcoded parameters and are very strict. On the other hand AI understands context, can figure out business logic, adapt the codebase. Logic flaws or performance bottlenecks rule-based scanners might miss, AI will put more effort into that. Static analysis limits Static tools scan for syntax errors, style violations, and basic security patterns using fixed rules. Always consistenst, very fast but might generate false positives, ignore business logic, and require manual rule overrides. How often did You use @typescript-error 🙂 Do You code for the linter to pass, logic to…
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Code mode for mcp servers and llms
Code mode for mcp servers is about LLM writing and calling the code to use a proper MCP instead of calling it directly with the whole context. It makes the call a lot smaller, no overhead is passed, just the basics that are required to call the proper MCP method. Just as You do in code, method or a function, proper parameters, everything validated and… BAM ! We are returning a context that LLM uses in further stages. Anthropics wrote… This is really nice looking but only for a huge models with a 1kk tokens of a context. We need to remember that this is not possible on any kind…
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How to use Context7 mcp server
What is Context7? Context7 is an open-source MCP server that provides real-time, version-specific documentation and code examples for over 50,000 libraries. It integrates seamlessly with AI tools like GitHub Copilot, Claude, or custom LLM agents. Why use it? How to use Context7 mcp server ? You can play around on the main page of the project or simply use a curl to fetch the data You wish so look up : context7.com/api/v2/docs/code/vuejs/pinia?topic=log&tokens=666 How to Configure Context7 How to use Context7 is really simple, just add a proper config entry and the plugin You use should pick it up instantly. Maybe reload the app if in need. 1. Install the Context7…
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Tokenization and embedding of song lyrics “We will, we will…”
Tokenization and embedding of song lyrics “We will, we will…” i know you know how it ends. but have You wondered what would an LLM say ? Let us find out. I want to ask the Clause Sonnet 4.5 about the embeddings, tokenizations and probability of figuring out the lyrics for “We will, we will…” prompt 🙂 Can you show me the tokenization, embeddings, emtadata and probabilities for “We will, we will …” Tokenization Any kind of machine learning uses numbers in the back stage. The text is split into tokens, each mapped to an ID ( example values ) : Token ID Token Text Type Position Length 1234 We…
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What is a token in AI ? Prompt examples
What is a token in AI ? – A piece of text, usually a word that we send to the LLM. The same goes for when we get a response ( usually number of words = number of tokens, roughly). How to use it ? Since we pay for it , rather cautiously. We want to send the minimum and get the maximum out of every request. Pretty much the basics of economics. Below are couple of prompts i used to analyze my tokens usage. Something are obsiously hallucinations but on the other hand we get a prety decent breakdown of all the data i did send for that coding…
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RICO prompt model
RICO prompt model is a simpler RICECO method and is NOT based on one of the penguins from the madagraskar…. You won`t remember this because of him 🙂 Rico is one of the simpler methods out there, not much complicated, just like a good kaboom 🙂 Simple and easy to remember. Rico can help You start Kabooming more effective prompts for any AI. RICO is a simple framework that helps you structure prompts to help and guide AI so it understands exactly what you want. The RICO Method: A Kaboom Framework for Better AI Prompts Recently one skill is becoming more and more valuable: prompting. Good prompts lead to clearer,…
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What is an MCP (Model Context Protocol) data format ?
A regular MCP (Model Context Protocol) format follows JSON-RPC 2.0 encoded in UTF-8 standard. The format is design to easily integrate different tools used with AI and LLMS like Context7 ( used by visual code or intellij). Servers like Context7 are designed to integrate real-time, version-specific documentation and code examples directly into AI or coding assistant prompts to improve code accuracy and developer productivity. MCP naming conventions MCP naming conventions are usually : use lowercase letters, hyphens, or camelCase without spaces or special characters. Example filenames : The folder structure may organize docs by library by topic, component or any other phrase or category: MCP documentation file format MCP Context7…
