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Dealing with github copilot errors
Dealing with github copilot errors is pretty irritating. Especially when dosing some more complex stuff and suddenly “Bam”. Red message. Easiest thing to do ? Switching the model family often works because some models have different capacity pools or stricter preview limits. GitHub documents vaguely that if you are rate limited, you can wait and try again. Just type “continue” and keep your fingers crossed. Otherwise check usage patterns, change the model, or contact support. Common github copilot errors Some run of the mill You probably know already, just put them together as a ‘review’ : Why changing the model helps ? Changing from one model family (vendor) to another…
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AI and LLM articles – links to read
Some links and articles i think are worth Your while to read and get Your own opinion. The Top 100 Gen AI Consumer Apps — 6th Edition | Andreessen Horowitz When Small Models Outperform the Giant: A Practical Guide to Picking AI Brains – DEV Community https://builtin.com/data-science/step-step-explanation-principal-component-analysis How to run mcp inspektor modelcontextprotocol/inspector: Visual testing tool for MCP servers And some more for some bed light reading 🙂 https://techtrenches.dev/p/the-great-software-quality-collapse Vertical Slice Architecture PQ4R Method: 6 Steps to Learn Effectively | 1Focus
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Jevons paradox in AI workplace
Jevons paradox in AI workplace Jevons paradox in AI workplace is introduced at work with a simple promise: do more in less time. In practice, the result are messy and stack the work. Jevons paradox is the idea that when something becomes more efficient, people and organizations often end up using more of it. In the AI workplace that can mean faster tools, automated work that do not always reduce workload. They can also expand expectations, volume, and ambition to utilize the improved (AIed ?) processes. Be aware At first glance, this sounds contradictory and just wrong. If a team can draft emails, summarize meetings, and generate reports in minutes,…
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Need to know old boy. Principle of Minimum Access for LLMs
Principle of Minimum Access for LLMs can be described by Timothy Dalton as James Bond in “The Living daylights”. Bond says the phrase “Sorry old boy, section 26, paragraph 5, need to know.” to a fellow agent and drives off escorting a VIP – very important target. Behind the scene is a practical idea that fits modern AI systems very well: an LLM or agent (as in the movie) should only be given the minimum access it needs to do its job. Not more, not less. Bare minimum. Of course Mythos probably could jailbreak anyway but still… controll is the best form of trust ? This is not just a…
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Structure for instructions, agents and skills
This all is the provide a nice clean idea on how to store your files so You can make Your AI assistant / LLM network understand what and how to do. Acording to Your more or less strict rules. This should help You to achieve more repeatable results as expected. The problem : generic answer Do not confuse with generic functions, those rock ! Out-of-the-box, any LLM (here copilot) creates generic code. You could call it ‘vanilla’ flavour. It doesn’t know your conventions, library preferences patterns / anti patterns. This results in something that might work but is hard to maintain, totally different then the rest of the lot and…
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Protect your work with poison ?
Protect your work with poison ? Is there no other way around. Robots.txt ? Nobody cares. Copyrights ? LOL. Fair use policy ? As long as i don`t get caught. Protect your work with poison is the oldest trick in the book, especially by plants. Most of them have to be cooked, to be eaten and digested with benefit for us. Why not doing it with our work ?I am thinking about healthy amount of protecting our work. Does it mean anyone making a rembrandt style photo should pay the author of that style ? For 70 years ? How much and how long ? Why money at all ?…
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Yet another basic AI glossary part 1
AI & Machine Learning Glossary for Beginners Yet another basic AI glossary part 1. This is base what i need to learn better and understand all that “AI” and “LLMs”. Feel free to go through all of it and dive deeper on those subjects. Defining here ai concepts, ideas, math functions, slang and anything that might be helpful in better understanding “the whole lot”. 1. Logit A logit is the raw output we get from a model before any functions are applied. Before the softmax functions. During classification logit shows the confidence of the model about everypossible output. 2. Logit Definition (Mathematical View) In math logits are real numbers output…
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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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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…
