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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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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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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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Losada coefficient at life and work
The Losada coefficient at life and work is pretty much the same theory. Also known as Losada ratio or critical positivity ratio. Proposes a fixed ration between positive and negative interactions / emotions. Supposedly distinguishes positive from negative individuals or teams.We should have proportionally MORE POSITIVE INTERACTIONS so in the long run we will be happy.Originated in 2005 paper by psychologists Barbara Fredrickson and Marcial Losada, who calculated a threshold of ~2.9:1 and upper? limit around 11.6:1 Losada ratio in coportate In highly skilled IT software development teams ( or any other team for that matter) this balance shows up in code reviews, stand‑ups, meetings, emails, design discussions and production…
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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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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……
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97 Things Every Data Engineer Should Know – book review
97 Things Every Data Engineer Should Know review will be a positive one. This style of books is currently my favorite. Might get another one from the series 🙂 It is bits and pieces of knowledge You can digest easily. Scattered across multiple disciplines teachings are of a principle design. Book is technologically agnostic, meaning rules, law, principles and methodologies presented You can use with any framework or system. It is like the design principles. Great read, would recommend. 97 lessons to pick from The book is all about best practices, system design, queues, asynchronouse communication and many more. You can easily read it day by day when You „meditate”…
