ai

My LLM Codegen Workflow Atm

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TL;DR: Harper Reed shares a code generation workflow using LLMs, focusing on brainstorming, planning, and execution. He details two approaches: greenfield (new projects) and incremental (existing codebases). His steps involve honing ideas with a conversational LLM, creating detailed specs, and employing tools like Claude and Aider for execution. Despite the efficiency, he highlights limitations, such as solo work and potential environmental impacts of AI. He encourages skeptics to explore AI's benefits through resources like Ethan Mollick's book, aiming for a collaborative coding experience.

https://harper.blog/2025/02/16/my-llm-codegen-workflow-atm/

Augment Code: An AI Coding Tool for ‘Real’ Development Work

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Augment Code is an AI coding tool designed for professional developers, providing support for code completion and improvements in Visual Studio Code. Unlike tools like Bolt, which target non-professionals, Augment focuses on enhancing productivity for experienced coders. It integrates seamlessly and offers features like project comprehension and code style adaptation. The tool suggests code improvements and facilitates efficient coding practices, optimizing the developer's workflow through quick feedback and contextual assistance.

https://thenewstack.io/augment-code-an-ai-coding-tool-for-real-development-work/

The 6 Best AI App Builders in 2025

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TLDR:

In 2025, the top six AI app builders are:

  1. Softr – Fast app generation, free and paid plans ($59/month).
  2. Microsoft Power Apps – Flexible with AI editing, starts at $20/user/month.
  3. Quickbase – Enterprise-grade functionality, starts at $35/user/month.
  4. Airtable Cobuilder – Integrates with Airtable for quick data views, free and paid options.
  5. Create – Builds apps from a single prompt, starting at $19/month.
  6. Databutton – Offers control using an AI agent, pricing starts at $20/month.

Criteria for evaluation included prompt interpretation, automatic functionality setup, no-code usability, customization tools, and easy publishing. The landscape is evolving rapidly, with platforms focusing on enhancing user experiences in app development through AI.

https://zapier.com/blog/best-ai-app-builder/

New Junior Developers Can’t Actually Code

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New junior developers rely heavily on AI tools like Copilot for coding, producing functional code quickly, but often lack deep understanding of the underlying principles. They struggle to explain why their solutions work or to consider edge cases, indicating a loss of foundational knowledge. Unlike previous generations who engaged with resources like Stack Overflow, current practices favor quick fixes over thorough learning. To address this, developers should adopt a learning mindset with AI, engage in meaningful discussions, enhance code reviews to emphasize understanding, and build projects from scratch to deepen their knowledge. The challenge is to balance efficiency gained through AI with the necessity of solid coding fundamentals.

https://nmn.gl/blog/ai-and-learning

You Are Using Cursor AI Incorrectly…

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Users are misusing Cursor AI by treating it as a search engine, under-specifying prompts, and misapplying its autonomous features. Key advice includes leveraging Cursor Rules to build a library of prompting rules instead of just implementing code. This “stdlib” can improve interactions with Cursor by teaching it through loops of corrections and updates. Successful outcomes require structured requirements discussions, rule creation, and automatic handling of tasks like commits. The goal is to automate software development processes, enabling users to scale their workflows with Cursor's help.

https://ghuntley.com/stdlib/

AI Coding Assistants Limited but Helpful, Developers Say

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AI coding assistants are deemed limited yet beneficial by developers at the DeveloperWeek conference. While tools like GitHub Copilot, Tabnine, and JetBrains AI are praised for speeding up development and offering useful code snippets, concerns persist about their maturity, accuracy, and reliability, particularly in handling complex code scenarios and avoiding “hallucinations.” Attendees believe these tools will improve with time, but users must possess significant technical knowledge to effectively utilize them.

https://www.infoworld.com/article/3825429/ai-coding-assistants-limited-but-helpful-developers-say.html

Silicon Valley’s Next Act: Bringing ‘Vibe Coding’ to the World

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Silicon Valley introduces “vibe coding,” a term coined by Andrej Karpathy, blending AI tools like Replit Agent for simpler software development. This approach makes coding more accessible, especially for beginners, but experts warn of potential downsides such as lack of understanding of system architecture and technical debt. While AI can expedite coding, it may struggle with ongoing project maintenance. Industry leaders, including Sam Altman and Mark Zuckerberg, foresee significant changes in software engineering by 2025 as vibe coding evolves.

https://www.businessinsider.com/vibe-coding-ai-silicon-valley-andrej-karpathy-2025-2

Zed Now Predicts Your Next Edit With Zeta, Our New Open Model

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Zed introduces Zeta, an open-source edit prediction model that anticipates user edits for efficient workflow. Users can quickly apply predictions using the tab key, enhancing the editing experience without disrupting existing functionalities. Zeta utilizes advanced techniques like supervised fine-tuning and direct preference optimization to improve accuracy and minimize latency with speculative decoding. Users are encouraged to contribute to Zeta's dataset for ongoing improvements. Currently available for free during public beta, Zed aims to evolve by incorporating community feedback and refining its AI capabilities.

https://zed.dev/blog/edit-prediction

Coding’s New Dawn: How AI Assistants Are Reshaping Software Development

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AI assistants like GitHub Copilot, Claude 3.5, GPT-4, and Google’s Gemini are transforming software development, enabling users to code quickly using natural language commands. These tools provide rapid solutions, reducing the learning curve for beginners while improving efficiency for seasoned developers. Despite outstanding performance in benchmarks, real-world application presents challenges, highlighting the need for human expertise in complex projects. The rise of AI coding tools is democratizing development, leading to innovative new products and inspiring investment, but also raising concerns about over-reliance on AI-generated code. Ultimately, a balance between AI capabilities and human insight is essential for future software creation.

https://cybernews.com/ai-news/ai-assistants-reshaping-software-development/

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

5 Best AI-Powered Git Commit Message Tools Compared

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Comparison of 5 AI-Powered Git Commit Message Tools:

  1. GitHub Copilot – Suggests commit messages based on code changes. Pros: reliable, free tier; Cons: basic messages, usage limits.

  2. CursorAI – Similar to Copilot but less accurate. Pros: works out of the box, free tier; Cons: less accuracy, usage limits.

  3. czg – Uses Commitizen framework for structured messages. Pros: supports emojis, open-source; Cons: complex configuration for non-developers.

  4. OpenCommit – CLI tool generating commit messages quickly, supports various models. Pros: open-source, fun emojis; Cons: sometimes poorly formatted messages.

  5. AI Commits – CLI tool for automatic commit messages based on code changes. Pros: easy to install, open-source; Cons: no Ollama support.

Choose a tool based on workflow preferences; most are free to try.

https://www.hongkiat.com/blog/best-ai-tools-for-git-commit-messages/

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

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