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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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Psychological safety vs producive stress. How not to go #toxic.
Psychological safety vs producive stress is a conflict of interest. People usually think that much „safety” can lead to laziness ? Don’t rest on your laurels as they say ? On the other stress and some level of danger motivates us to harder work. Workplaces need a certain level of pressure to move forward. Deadlines, feedback, and responsibility all matter. But pressure is not the same as panic, and motivation is not the same as fear. The real challenge is to create an environment where people feel safe enough to speak honestly, while still being stretched enough to grow (or just get exploited and run down the mill?). Safety leads…
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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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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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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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What Jack Oneill teaches us about efficiency
What Jack Oneill teaches us about efficiency, underneath his sarcasm, is ruthlessly practical. Listening to Jack You can easily figure out the most important things to do simple… efficient… are : Jacks worldview reminds us that intelligence isn’t just about knowing, it is rather wisdom anyway, but more so it is about acting clearly in the face of confusion. In a world rewarding complexity, that kind of simplicity is both rebellious and profound. Colonel (later General) Jack O’Neill ( two Ls) from Stargate SG‑1 is remembered for his sarcasm and not liking scientists. Beneat dry humor lies, a surprisingly deep, wisdom rooted in simplicity, straight talk, and cutting through noise…
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Typescript perfect sync – 3 tricks to keep it tight
Typescript perfect sync can be kept using couple of strategies. Most of us start with simple union types, like this: const ScenarioStatus = 'success' | 'fail' | 'error' | 'other'; So far so good. That works great… until you need to iterate over those values for rendering, validation, or other logic. And now You are stuck with two separate definitions: one array for logic, and one string union for the type. Sooner or later, they’ll fall out of sync and provide headaches to people. You could go for en enum…. Below behold the three TypeScript tricks to make your code tight, hard to break, cleaner, safer, DRYer and resistant to…
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Data contracts. Building universal data access proxy api
Data contracts usually requires us to build around them a universal data access proxy API for users to consume. API utilizing proper data contracts, negotiated with different teams, acts as a unified gateway providing the necesities. Allows access to data sources like databases, REST APIs, GraphQL endpoints or other file systems. One api to rule them all Steps to build data contracts for proxy api You could try and adopt a similar flow for creating such access points, even make a template in JIRa so You will know where to get proper data and how to acquire it… or maybe expose the library and just aprove properly looking merge requests……
