coding

Review: Zencoder Has a Vision for AI Coding

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Zencoder is an emerging AI coding assistant that utilizes “Repo Grokking” to analyze entire codebases for better context in code generation and repair. It features error-corrected inference and supports over 70 programming languages. While Zencoder's ability to process entire repositories enhances code generation quality, it currently lacks the capability to modify multiple files simultaneously. The product shows promise but is still developing compared to competitors that can handle more complex tasks. Pricing starts with a free plan, advancing to $19-$39 per user for business and enterprise options.

https://www.infoworld.com/article/3820199/review-zencoder-has-a-vision-for-ai-coding.html

Tech’s Dumbest Mistake: Why Firing Programmers for AI Will Destroy Everything

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Firing programmers for AI is a huge mistake. It leads to underprepared new coders, companies regretting layoffs due to AI-generated software failures, and increasing rarity and costs of skilled programmers. Companies replacing human engineers with AI risk chaos, as untrained staff will lack essential skills, causing serious operational issues. In the end, firms may struggle to rehire skilled workers, facing skyrocketing costs for the best talent. Overall, tech is harming its own ecosystem by underestimating the value of human engineers.

https://defragzone.substack.com/p/techs-dumbest-mistake-why-firing

We Are Destroying Software

Destroying software: ignoring complexity, using complex builds, bloated dependencies, discouraging learning (reinventing wheels), neglecting API compatibility, unnecessary rewrites, chasing trends, underestimating existing libraries, preferring standards over tailored solutions, dismissing code comments, viewing coding as mere engineering, complicating simplicity, prioritizing speed over quality, risking loss of joy in hacking.

https://antirez.com/news/145

Coding the Hard Way? I Tried 9 Best AI Code Generators

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Summary: The article discusses the author's experience with AI code generators, highlighting their ability to simplify and enhance coding tasks by translating plain English into executable code. The author tested nine top AI code generators: ChatGPT, GitHub Copilot, Gemini, Pieces for Developers, Crowdbotics Platform, Tune AI, Gemini Code Assist, Sourcegraph Cody, and Amazon CodeWhisperer, analyzing their strengths and weaknesses in terms of accuracy, usability, and integration. Despite frustrations with error handling and outdated syntax, these tools significantly reduce manual coding effort and optimize workflow for both experienced developers and novices.

https://learn.g2.com/best-ai-code-generators

Chat Is a Bad UI Pattern for Development tools—Daniel De Laney

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Chat is ineffective for development tools; precision is needed in programming. AI was expected to streamline this, allowing plain English for coding, but current AI tools fail to deliver usable software. Programming requires clarity and organization, not conversation. Effective tools will prioritize structured documentation over chat, leading to better software development. The first company to recognize this will lead the next wave in AI development tools.

https://danieldelaney.net/chat/

Bill Gates on Coding, Math and Generative AI

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Bill Gates emphasizes the importance of coding and math skills even in an AI-driven world, arguing they are essential for understanding AI's workings. In an Axios interview, he compares learning math to coding, asserting that foundational knowledge helps comprehend AI's strengths and weaknesses. Gates critiques the notion of banning AI in education, advocating for its integration alongside traditional assessment methods. He also reveals a personal note in his new book, recognizing traits of autism spectrum disorder, hoping to inspire similar children.

https://www.axios.com/2025/02/03/bill-gates-coding-matters-generative-ai

Developer Philosophy

Developer Philosophy @ Things Of Interest

  • Ground-up rewrites are risky; avoid situations leading to them.
  • Aim for 90% completion in 50% of the time; polish and testing take longer than expected.
  • Automate adherence to best practices rather than relying solely on manual enforcement.
  • Prioritize handling edge cases and unexpected data.
  • Write code that is simpler and easily testable with clear interfaces.
  • Code should be obviously correct and handle potential failures gracefully.

https://qntm.org/devphilo

Five Coding Hats

Five coding “hats” symbolize different approaches to programming based on context:

  1. Captain’s hat: Methodical, careful coding for high-stakes situations.
  2. Scrappy hat: Lean, minimal code for prototypes with minimal testing.
  3. MacGyver hat: Quick, messy solutions to test ideas without concern for code quality.
  4. Chef’s hat: Focus on making code aesthetically pleasing, sometimes at the expense of efficiency.
  5. Teacher’s hat: Prioritize code clarity for educational purposes over performance.

Adopting the right “hat” aligns coding style with goals.

https://dubroy.com/blog/five-coding-hats/

Sourcegraph Automates ‘soul-crushing’ Tasks With AI Coding Agents

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Sourcegraph launches AI coding agents to automate tedious tasks in software development, enhancing efficiency and allowing developers to focus on complex work. Their initial offerings include the Code Review Agent, available in Early Access, which streamlines code reviews and other processes. Industry leaders like Indeed, Booking.com, and Priceline report significant productivity gains and bug reductions using these agents, signaling a cultural shift towards integrating AI in development workflows rather than replacing human roles. Sourcegraph envisions a collaborative future where humans and AI work together effectively in coding.

https://www.developer-tech.com/news/sourcegraph-automates-soul-crushing-tasks-ai-coding-agents/

The Best AI for Coding in 2025 (and What Not to Use

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Best AI coding tools of 2025 identified via two years of testing. Recommended: ChatGPT Plus ($20/mo) and Perplexity Pro ($20/mo) for reliability. Avoid DeepSeek R1 and other poorly performing AIs. ChatGPT Free offers decent functionality but is limited. DeepSeek V3 shows promise but lacks knowledge in some areas. Overall performance of widespread AIs like Microsoft Copilot is subpar. AI bootstrapping is improving swiftly.

https://www.zdnet.com/article/the-best-ai-for-coding-in-2025-and-what-not-to-use-including-deepseek-r1/

I Tested DeepSeek’s R1 and V3 Coding Skills

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DeepSeek's AI models, V3 and R1, were evaluated on coding tests, showing impressive performance relative to other AIs despite lower infrastructure usage. V3 excelled in creating a WordPress plugin and debugging, while R1 struggled with reasoning and edge cases. Overall, V3 outperformed R1, achieving three wins to R1's two. DeepSeek represents a notable entry in the AI coding space but still exhibits room for improvement.

https://www.zdnet.com/article/i-tested-deepseeks-r1-and-v3-coding-skills-and-were-not-all-doomed-yet/

Discovery Coding

Discovery Coding: A coding practice where programmers write code first to understand a problem rather than outlining or planning. Often perceived as messy, discovery coding contrasts with structured approaches like outlining. Despite cultural preferences for structured methods, discovery coding fosters unique insight by exploring system constraints. The writing community accepts discovery writers; programming should also embrace discovery coding as a valid method, acknowledging different thinking styles without ranking them.

https://jimmyhmiller.github.io/discovery-coding

Hacker News Discussion about Discovery Coding TLDR: Discovery coding emphasizes exploration over rigid planning, allowing developers to understand problem spaces through iterative prototyping. Critics argue it risks shipping incomplete solutions, while proponents highlight flexibility, rapid learning, and adaptability. Balancing exploratory coding with structured testing and refactoring is key to effective software development.

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