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How AI will change software engineering
https://leaddev.com/career-development/how-ai-will-change-software-engineering
#aiml #software #softwaredevelopment #developers
Great example when software/data engineering lack of skills will cost you dozens thousands dollars on a few clicks in #Cloud. Senior #Data Engineer in undisclosed company changed Cloud Storage Lifecycle Policy and caused 600 milion files from Standard Storage to Tier 2 Archive, which caused cost about $60k. Those fancy hidden costs in cloud. BTW this is hidden in Google cost calculator under advanced section. #dataengineering #Software #softwaredevelopment
Pichaimani Rajesh Kumar co-hosts a Workshop on RAG Solutions and AWS July 23rd at Nebraska.Code().
We all aim to make our programs faster, but have you ever tried doing the opposite? In this article, @carlk demonstrated how a simple nested loop can create a program that runs longer than the universe's lifetime. He dove into concepts like tetration (yes, it goes beyond exponentiation), 5-State Turing Machines
https://towardsdatascience.com/how-to-optimize-your-python-program-for-slowness/
Big moves in the #OpenJDK ecosystem!
Eight new JEPs have advanced from JEP Draft to Candidate status - and four are on track to be finalized after their preview rounds.
Stay tuned as #Java continues to evolve: https://bit.ly/4itvZ71
"I have taste and you don't" is a great way to never get anybody to agree with you. So is "your solution is not the way I would have done it."
The only way to get off doing 12-hour shifts at the feature factory is to defy its logic.
The Agile vs. Waterfall Trolley Dilemma
You find yourself standing before a complex, control panel in a dimly lit room. Two paths diverge before you: one labeled "Agile", the other "Waterfall". A single, rusted lever protrudes from the center.
To your left, the Agile path, a lively bunch of developers are sprinting towards a rapidly approaching deadline, their hair on fire, eyes wide with panic. They're still iterating and refining their product, but time is running out. If you pull the lever, you'll derail their momentum, forcing them to focus and deliver something actually finished and useful on time. But if you don't, they might sprint off the cliff of completion, leaving behind a half-baked mess.
To your right, the Waterfall path, a team of meticulous, bespectacled analysts are methodically working their way through a towering stack of Gantt charts. They're following the plan to the letter, but a massive boulder of a feature has just rolled onto their critical path. If you pull the lever, you'll redirect their focus, allowing them to adapt and deliver the essentials. But if you don't, the boulder might crush their entire project under an avalanche of scope creep.
What do you do? Pull the lever and disrupt the status quo, or leave things as they are and hope for the best?
"We recently released Claude Code, a command line tool for agentic coding. Developed as a research project, Claude Code gives Anthropic engineers and researchers a more native way to integrate Claude into their coding workflows.
Claude Code is intentionally low-level and unopinionated, providing close to raw model access without forcing specific workflows. This design philosophy creates a flexible, customizable, scriptable, and safe power tool. While powerful, this flexibility presents a learning curve for engineers new to agentic coding tools—at least until they develop their own best practices.
This post outlines general patterns that have proven effective, both for Anthropic's internal teams and for external engineers using Claude Code across various codebases, languages, and environments. Nothing in this list is set in stone nor universally applicable; consider these suggestions as starting points. We encourage you to experiment and find what works best for you!"
https://www.anthropic.com/engineering/claude-code-best-practices
While I wish I could start coding sooner, I can't change the fact that improving our efficiency by f.ex. 20% as a team through all the things I've mentioned in this thread, is just quantifiably more than I could contribute as an individual through writing code alone.
Despite the feedback and progress so far, it still feels weird not writing code yet...
It'll be fine, and I know I'll get to it soon enough, I'm just looking forwards to contributing to a few parts of the codebase 
By collaborating closely with, and with thanks to the product owner, I've been able to apply structure and standards, that I hope will allow us to start measuring velocity and start estimating capacity for the future. If successful, it should be a win-win, as we'll be able to give better estimates to our clients, and avoid crunch for the team.
Cost of this has been spending my evenings the first 2 weeks making agile visualisations, docs and (re)writing, linking and organising work items (tickets/issues) to the point where I'm now the author of 2/3 of our total count 
I'm amazed at how receptive and supportive everyone has been so far, which hopefully is a sign that I'm doing the right things. Low-key also feels like, they're just happy to have a tech lead again after being without one for a while.
Hope this will continue, never had a job I felt this welcome in, and happy doing before 
It's been interesting to see how I've had to change my approach as a tech-lead, but as I now have different responsibilities than that of a senior developer it sorta makes sense?
As expected, it's already changed several times as I've had to re-evaluate it throughout my interview and onboarding process, but as of right it's three main points, in order:
Though doing this as I've been getting to know the team, going to conferences and meeting clients and integration partners has been a lot 
"Most guides to docs like code, even the ones for non-devs, assume you have some developer knowledge: maybe you're already using version control, or you've encountered build pipelines before, or you're working alongside developers.
This guide is for the people who read that paragraph and wished it came with a glossary. This is docs like code for people who don't know what git is and have never installed VS Code.
This post explains terminology and concepts, to help you get a mental model of what's going on. If you prefer to dive in and pick up concepts as you go, skip straight to the tips in How to learn, and come back to the conceptual info as needed."
The AI Overload: Are Software Tools Losing Their Core Functionality?
In a landscape flooded with AI features, developers are voicing frustration over the stagnation of essential software tools. As AI buttons proliferate, core functionalities are increasingly neglected,...
https://news.lavx.hu/article/the-ai-overload-are-software-tools-losing-their-core-functionality
Meet Deebo: The AI Debugging Partner Revolutionizing Software Development
In the evolving landscape of software development, Deebo emerges as a powerful autonomous debugging system, designed to work alongside AI coding agents. By running parallel experiments and delivering ...
The Art of Crafting Perfect Pull Requests in Software Development
In the world of software development, the pull request (PR) is a critical component that can make or break a project's success. Understanding the anatomy of a perfect pull request not only enhances co...
https://news.lavx.hu/article/the-art-of-crafting-perfect-pull-requests-in-software-development
"[T]hose claiming we're mere months away from AI agents replacing most programmers should adjust their expectations because models aren't good enough at the debugging part, and debugging occupies most of a developer's time. That's the suggestion of Microsoft Research, which built a new tool called debug-gym to test and improve how AI models can debug software.
Debug-gym (available on GitHub and detailed in a blog post) is an environment that allows AI models to try and debug any existing code repository with access to debugging tools that aren't historically part of the process for these models. Microsoft found that without this approach, models are quite notably bad at debugging tasks. With the approach, they're better but still a far cry from what an experienced human developer can do.
(...)
This approach is much more successful than relying on the models as they're usually used, but when your best case is a 48.4 percent success rate, you're not ready for primetime. The limitations are likely because the models don't fully understand how to best use the tools, and because their current training data is not tailored to this use case."
#Python Tariff - Finally a solution for dependencies stealing local CPU cycles. Make code great again.
Vad gör en lead software engineer?
Det beror på, och det ska nog vara lite löst i kanterna. Sofia Larsson från grannpodden Developers! - och innehavare av titeln - förklarar.
Ett utdrag ur Kodsnack 638, lyssna där poddar finns och på kodsnack.se!
#podcast #softwaredevelopment #titles
How is #GenerativeAI reshaping #SoftwareDevelopment & #SoftwareTesting?
In this #InfoQ #podcast, Adam Sandman talks about automation, faster prototyping, collapsing traditional roles, AND the challenges of increased defects and ethical considerations.
Listen now: https://bit.ly/3Esiruz
