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Nov 15Sun
  1. Hacker News AI22

    We Need Guidelines for Learning and AI

    The author argues that while AI can provide cheap, patient tutoring and on-demand explanations, having it do your homework is self-defeating because you can't learn without doing the work, drawing on Carl Hendrick's analogy between autopilot and degraded pilot skill to warn against over-dependency. He proposes tentative guidelines: don't use AI on your first or last attempt, but do use it to suggest alternative ideas and resources and to fix confusion and mistakes. AI can assist the "see" and "feedback" parts of the learning loop, but cannot replace the middle step of doing the work yourself.

  2. Hacker News AI22

    Nonna's ragù: AI skill files only get you started

    Andrea Azzini argues that a shared AI skill file is like a family ragù recipe: readable and tweakable, but the value comes from your own repeated corrections over time. Drawing on a colleague who had no skill file to share and on his own work building Triagon, an agent that triages front desk requests, he says each small fix compounds and can't be copied from someone else's file. Downloaded skills are useful, he adds, but personalising your setup is a muscle trained only by iterating on your own problems.

Nov 11Wed
  1. Hacker News AI38

    AI proved your code correct. What did it actually prove?

    Keller AI explains how formal verification works, using a sorting example and Euclid's equilateral-triangle construction to show how proofs combine definitions, assumptions and prior results, then applies the approach to a document-download authorization handler. The article stresses that a checked proof only establishes a result within a mathematical model, so its value depends on whether the model's assumptions match reality and whether the specification captures the intended requirement, citing a revocation-versus-session example and timing side channels. It also reports an unreproduced synthetic experiment in which the best of seven automated reviewer setups approved 1 of 88 sealed rule-breaking changes.

Nov 10Tue
  1. Hacker News AI20

    Technical Writing in the AI Era

    The author argues that while AI is useful for research and gathering content, technical documents meant for human readers—such as RFCs and RCAs—should still be written by humans, because AI-generated documents are bloated, inconsistent in terminology, and lack awareness of the reader's flow. He notes that auto-generated API documentation is now largely redundant since AI can read source code or OpenAPI specs directly, and that high-quality human-written documents matter more than ever as teams ship more code.

Nov 9Mon
  1. Latent Space40

    [One Million AI Engineer Special] How Stanford Teaches AI-Native Engineering — Mihail Eric, Monaco

    In the second run of Stanford's CS146S, Mihail Eric brought open-source maintainers into the course, having students learn an unfamiliar repo, propose a change, and explain the patch to the person who might merge it. In this interview marking one million subscribers on the AI Engineer YouTube channel, he walks through the ten-week syllabus and discusses which lessons held up with his own engineering team at Monaco, including building agent workspaces, buying code review tools, and giving an on-call agent access to Datadog and GitHub.

Nov 6Fri
  1. Hacker News AI38

    How AI Is Changing Software Development in 2026

    This article surveys how AI coding agents have moved from autocomplete-style assistants to tools that can inspect repositories, modify multiple files, run commands and tests, and iterate on their own changes, citing JetBrains and Stack Overflow survey figures on adoption. It argues that faster code generation does not automatically mean faster delivery, pointing to METR's 2025 study in which experienced developers took 19% longer with AI allowed while believing they were faster, and stresses review, security, and human judgment. It also distinguishes prototypes, MVPs, and production-ready products, and offers guidance for introducing agents into workflows and for clients evaluating AI-assisted teams.

  2. Hacker News AI22

    AI Makes Building Easy. Failing First Is the Hard Part

    This blog post argues that AI has made building products cheap and fast, but the harder discipline of testing whether anyone wants them has become easier to skip, citing Zappos' low-cost shoe experiment, Humane's $699 AI Pin, and a founder who shipped 128 releases with zero external users. It urges founders to write a one-sentence hypothesis, define success and failure before shipping, and set a kill date.

Nov 3Tue
  1. Hacker News AI38

    Code-First Internal Tools vs. Low-Code in the Age of AI

    OpenRun, an open source self-hosted GitOps platform for deploying web apps and internal tools declaratively, argues that AI coding tools have removed most of the speed advantage of low-code platforms' visual editors, since generating a small app from a prompt now takes about as long as assembling it on a canvas. The post contends that low-code downsides remain—proprietary app formats, editor-only editing, limited testing and refactoring, per-user pricing and security features gated to higher tiers—while code-first apps benefit from reviewable pull requests, easier maintenance and no lock-in to a tool or model. It also notes that code-first internal tools still need a platform for hosting, SSO, access control, audit logs, database credentials and deployment, which is the gap OpenRun targets.

Nov 2Mon
  2. Hacker News AI22

    AI Can Write More of the Code. We Still Have to Engineer the System

    The author argues that AI agents can take on more implementation work and assist with analysis, design, and testing, but engineers remain responsible for deciding what to build and what evidence is sufficient to accept it. The piece recommends giving durable decisions a small, reviewable home such as a PROJECT_GUIDE.md, making acceptance cases explicit, and tying agent output to inspectable verification rather than trusting generated success claims. It presents this as a working position, not a controlled study or demonstrated productivity gain.

Oct 29Thu
  1. Hacker News AI20

    Rethinking the tech writing portfolio in the AI age

    The author argues that traditional writing-sample portfolios lose value in the AI age because LLM-reproducible artifacts no longer differentiate a knowledge worker, so portfolios should instead tell the stories behind the work, emphasize the kinds of problems one likes to solve, and showcase opinions and taste. He also advises against letting AI design the portfolio site, urging writers to choose fonts, colors, and copy themselves, and to present a broader range of tech-comms work such as automation scripts, linter rules, and agentic workflows.

  2. Pragmatic Engineer34

    Software engineering at Google & CS education with Titus Winters

    In this Pragmatic Engineer podcast episode, Titus Winters — lead author of Software Engineering at Google, former Google readability lead and Abseil builder, now a Senior Principal Scientist at Adobe — discusses how a small team refactored Google's roughly 250-million-line C++ codebase in 2010 using parsing, matching and code generation, and why Google made automated tests mandatory after Search's shipping cadence slipped to quarterly. He also argues the ACM/IEEE CS curriculum is badly out of date and should be largely rebuilt, and says AI has raised code output and outages while shipped features stay flat.

  3. Hacker News AI22

    AI Did Not Break Education, It Revealed the Bug

    In a PyCon Singapore talk, Georgi recounts leaving Singapore's education system after a year of teaching, where 36 of 40 students failed to grasp a topic but the syllabus required moving on, and argues AI has exposed a system optimized for correct answers rather than comprehension. He proposes schools borrow from open source: visible learning histories, AI output treated as a pull request to be reviewed, and teachers as maintainers of the learning environment rather than repositories of knowledge.

Oct 28Wed
  1. Hacker News AI36

    The half-life of AI knowledge

    The article argues that AI knowledge decays at different rates across layers: tools and products last months to a couple of years, techniques a few years, architectures five to ten years, and core ideas decades. It cites examples such as OpenAI's Agent Builder shutdown, the expiry of chain-of-thought prompt phrasing, and the long lineage of gradient descent (1847) and backpropagation (1986). It recommends learning threshold concepts so new releases read as variations on ideas already understood.

  2. Hacker News AI36

    Getting good at AI takes time and practice

    The post argues that becoming skilled with AI requires durable, long-term abilities rather than disposable prompt tricks, including supplying the right context, learning what models can and can't do, recognizing characteristic AI mistakes when reviewing code, and quickly skimming verbose output. It contends that new models improve mostly on tasks older models already handled well, so these skills remain useful, and that software's human-machine "centaur age" may last decades. The author concludes that deliberately avoiding AI likely leaves people behind, while going fully into AI is also a mistake since LLMs reward expertise best gained by hand.

  3. Hacker News AI33

    Feynman's wobbling plate and Threshold Concepts behind modern AI

    Luis, who has studied AI for about 10 years, argues that curiosity-driven play at the edge of one's knowledge, as with Feynman's wobbling plate, is the engine of real learning, and outlines eight "threshold concepts" that explain modern AI: learning from data, representations and embeddings, next-token prediction, attention, evals, post-training and RL fine-tuning, harnesses and agents, and recursive self-improvement. He cites Vygotsky's zone of proximal development, Meyer and Land's threshold concepts, and Bruner's spiral learning, and includes a study prompt usable with any LLM. The post ends with a paragraph promoting his AI coach, Zairai.

  4. Hacker News AI34

    Does AI Speed Up Development? Using the UKs GDS Service Standard as framework

    The author argues that AI mainly accelerates coding, which is only one part of digital service development, and uses the UK GDS Service Standard's 14 points to assess where AI actually helps. The tally is roughly 2 high-impact points (open source code and open standards, both code-heavy), 6 partial or mixed, and 6 low or none, with bottlenecks remaining in user research, organisational change, governance and procurement. The post concludes that speeding up the build step without widening the real bottleneck just moves the queue, and suggests the evolving Service Standard as a place to build evidence on AI's impact.