feeling behind as a programmer - CourtneyR.dev's 2nd Brain

feeling behind as a programmer 🔗 **Source:** [feeling behind as a programmer](https://www.youtube.com/watch?v=e-_743sQLa8) > [!note]+ Document Notes > tags: [programming, ai-tools, developer-productivity] > author: [MISSING] > source: YouTube > reference: https://www.youtube.com/watch?v=e-_743sQLa8 > date created: 2025-01-19 20:15 > > # 🏷️ Title: feeling behind as a programmer >  ## 🧰 Action Inbox - [H1] Focus on shipping features over learning every AI tool — stop FOMO-driven development #productivity - [H2] Set aside 1-2 days per month for AI tool evaluation — avoid daily hype consumption #time-management - [H3] Strengthen CI/CD, testing, and code review practices — these amplify AI benefits #fundamentals ## 🧭 Table of Contents - [📝 Summary](#-summary) - [📖 Main Content](#-main-content) - [🗒️ Notes](#️-notes) - [📚 References](#-references) - [🗣️ Paraphrase](#️-paraphrase) - [📰 Newsletter](#-newsletter) - [✅ Action Items](#-action-items) - [🧪 Hypotheses](#-hypotheses) - [📊 Methodology](#-methodology) - [❓ Questions](#-questions) - [🔮 Future Research](#-future-research) - [💡 Implications](#-implications) - [🧠 Learning Styles Assessment](#-learning-styles-assessment) - [📊 Accessibility Assessment](#-accessibility-assessment) - [🌍 DEIB Assessment](#-deib-assessment) - [✅ Logical Validity](#-logical-validity) - [🛡️ DARVO Analysis](#-darvo-analysis) - [🎯 Conclusion](#-conclusion) ### 🔗 References - [André Karpathy's Tweet](https://twitter.com/karpathy) - [The Mythical Man Month](https://en.wikipedia.org/wiki/The_Mythical_Man-Month) - [Dax's Response](https://twitter.com/DavidKPiano) ## 📝 Summary - A developer reflects on the intense FOMO and pressure to constantly learn new AI tools that dominated 2024 - André Karpathy's viral tweet about feeling "behind as a programmer" sparked widespread anxiety in the developer community - The core message: focus on shipping features and solving real problems rather than chasing every new AI tool or workflow - Traditional engineering skills (CI/CD, testing, documentation) are more valuable than ever when working with AI - Most experimental AI workflows will be abstracted away as the ecosystem matures ## 📖 Main Content The video addresses the widespread anxiety among developers in 2024 about falling behind due to rapidly evolving AI tools. The creator responds to André Karpathy's influential tweet expressing feelings of being "dramatically behind" as a programmer, where Karpathy listed numerous new AI concepts developers feel pressured to master. The speaker argues this represents a classic case of FOMO (fear of missing out) that's counterproductive. They emphasize that core programming fundamentals remain essential - AI tools still require human expertise to validate output, understand complex systems, and guide implementations effectively. The video draws parallels to historical technology cycles, noting that today's experimental AI workflows will likely be abstracted away as the ecosystem matures. The creator advocates for "just enough" AI adoption focused on shipping features rather than pursuing 10x productivity claims through agent orchestration. Key supporting arguments include references to "The Mythical Man Month" principles about why adding more resources (or agents) doesn't automatically solve problems faster, and observations that developers with strong CI/CD and testing practices are seeing the most success with AI tools. ## 🗒️ Notes #### ⏱️ [00:00](https://youtu.be/e-_743sQLa8?t=0) — [H1] The programming climate of 2024: hype and FOMO - Key idea: Developer culture dominated by pressure to constantly learn new AI tools - Evidence: "If I could describe what it was like to be a programmer in the year 2025, it would be hype and fear of missing out" #### ⏱️ [01:30](https://youtu.be/e-_743sQLa8?t=90) — [H2] André Karpathy's viral confession about feeling behind - Key idea: Even AI experts feel overwhelmed by the pace of change - Evidence: "I've never felt this much behind as a programmer. The profession is being dramatically refactored" #### ⏱️ [02:15](https://youtu.be/e-_743sQLa8?t=135) — [H3] The dangerous 10x productivity mindset - Key idea: Belief that not using all available AI tools represents incompetence - Evidence: "I could be 10x more powerful if I just properly string together what has become available... failure to claim the boost feels decidedly like a skill issue" #### ⏱️ [04:00](https://youtu.be/e-_743sQLa8?t=240) — [H2] Cataloging the new AI programming layer - Key idea: The new concepts are numerous but ultimately learnable - Evidence: Lists agents, sub-agents, prompts, contexts, memories, modes, permissions, tools as examples #### ⏱️ [06:30](https://youtu.be/e-_743sQLa8?t=390) — [H2] Learning has always been core to programming - Key idea: Adapting to new technologies is a fundamental developer skill - Evidence: "The best programmers are the ones that can adapt and constantly learn and progress" #### ⏱️ [07:45](https://youtu.be/e-_743sQLa8?t=465) — [H3] The prompting skills myth debunked - Key idea: Models have improved enough that detailed prompting isn't critical - Evidence: "You can type a single sentence that's not even grammatically correct and AI will still know what you want it to do" #### ⏱️ [09:20](https://youtu.be/e-_743sQLa8?t=560) — [H2] Most experimental tools won't matter long-term - Key idea: The ecosystem is moving so fast that current workflows will be obsoleted - Evidence: "When the dust settles... we're going to reach a point where these things won't matter anymore" #### ⏱️ [11:00](https://youtu.be/e-_743sQLa8?t=660) — [H1] Focus on shipping features, not tool mastery - Key idea: Core developer purpose remains solving real-world problems - Evidence: "Focus on just enough AI if it makes sense for shipping features and building useful things" #### ⏱️ [13:15](https://youtu.be/e-_743sQLa8?t=795) — [H2] Traditional engineering skills are more valuable than ever - Key idea: Fundamental development practices amplify AI tool effectiveness - Evidence: "Developers who have already invested in CI/CD, testing, documentation, and code review are having the most success" #### ⏱️ [15:30](https://youtu.be/e-_743sQLa8?t=930) — [H3] AI output requires validation like bad programmer code - Key idea: Treating AI as an unreliable programmer with infinite copy-paste ability - Evidence: "It's trained on a lot of bad code and it's going to output bad code" #### ⏱️ [16:45](https://youtu.be/e-_743sQLa8?t=1005) — [H2] The agent swarm fallacy - Key idea: Adding more agents doesn't solve problems faster, echoing Mythical Man Month - Evidence: "Adding manpower to a software project that is behind schedule delays it even longer" #### ⏱️ [18:20](https://youtu.be/e-_743sQLa8?t=1100) — [H3] Most AI workflow optimization is procrastination - Key idea: Perfecti ng AI setups often replaces actual productive work - Evidence: "The most common use of all these new knobs and levers is procrastination" #### ⏱️ [19:30](https://youtu.be/e-_743sQLa8?t=1170) — [H2] We haven't found the best AI workflows yet - Key idea: Current approaches are experimental and will evolve significantly - Evidence: "If we found it, we would absolutely all be out of a job... we're not there and we're not even close" #### ⏱️ [20:45](https://youtu.be/e-_743sQLa8?t=1245) — [H1] Practical advice: limit AI tool exploration to monthly sessions - Key idea: Structured approach to staying current without constant distraction - Evidence: "Set aside one day a month or two days a month where you take a look at what are the latest models" ## 📚 References - [André Karpathy Tweet Thread](https://twitter.com/karpathy) - [The Mythical Man Month by Frederick Brooks](https://en.wikipedia.org/wiki/The_Mythical_Man-Month) - [Dax's Twitter Response](https://twitter.com/DavidKPiano) ## 🗣️ Paraphrase - Developers in 2024 experienced unprecedented pressure to constantly learn new AI tools, creating anxiety about falling behind professionally - The fundamental skills of programming - understanding systems, validating code, breaking down problems - remain essential even when using AI assistance - Rather than pursuing maximum productivity through tool optimization, developers should focus on shipping features and solving real problems with appropriate AI integration ## 📰 Newsletter **Fighting Developer FOMO in the AI Era** The programming world spent 2024 in a state of constant anxiety about AI tools. This thoughtful reflection offers a much-needed reality check for WordPress developers feeling overwhelmed by the pace of change. **Key insight**: Your existing skills in testing, CI/CD, and code review are more valuable than ever when working with AI. These practices act as essential guardrails for validating AI-generated code. **Practical takeaway**: Instead of daily tool-chasing, set aside 1-2 days per month to evaluate new AI developments. Focus the rest of your time on what matters most - shipping features that solve real problems. The WordPress ecosystem has always valued pragmatic approaches over hype cycles. This same principle applies beautifully to AI adoption. Resources: [Learn WordPress](https://learn.wordpress.org/), [Developer Docs](https://developer.wordpress.org/), [WP-CLI](https://wp-cli.org/) ## ✅ Action Items #### DevRel - Developer advocacy: Create content addressing AI tool FOMO in developer communities - Education: Develop workshops on effective AI integration without workflow disruption - Developer experience: Document best practices for AI-assisted development workflows - Community management: Moderate discussions around AI tool adoption with balanced perspectives #### WordPress - Core: Evaluate AI tools for core development while maintaining code quality standards - Documentation: Create guidance on when and how to use AI for WordPress development - Community: Address AI anxiety in WordCamp talks and meetup discussions - Training: Develop learning paths that balance AI awareness with fundamental skills ## 🧪 Hypotheses - Developers who focus on shipping features while selectively adopting AI tools will outperform those constantly switching workflows - Organizations with strong CI/CD and testing practices will see better ROI from AI tool adoption than those without - The current experimental AI workflows will be largely obsoleted within 2-3 years by improved abstractions - Monthly rather than daily AI tool evaluation produces better long-term outcomes for developer productivity ### 📊 Methodology - Survey developers on time spent learning vs. shipping to test productivity hypothesis - Compare AI adoption success rates between teams with/without established engineering practices - Track longevity of current AI workflow tools to validate abstraction timeline ### ❓ Questions - How much AI tool learning time is optimal for maintaining competitiveness without hampering productivity? - Which fundamental programming skills will become more valuable as AI capabilities increase? - What indicators can help developers distinguish between lasting AI innovations and temporary hype? ### 🔮 Future Research - Long-term studies on developer career outcomes based on AI adoption strategies - Analysis of which 2024 AI tools actually persist and gain widespread adoption - Investigation of optimal team structures for human-AI collaboration in software development ### 💡 Implications - Practice: Shift focus from tool mastery to selective AI integration based on shipping needs - Education: Emphasize fundamental engineering skills as AI amplifiers rather than replacements - Career: Develop strategies for staying current without falling into constant learning traps - Team management: Balance AI experimentation time with feature delivery commitments ### 🧠 Learning Styles Assessment Reading level estimate: 9th-10th grade #### 👀 Visual learners - Present: Mind maps showing AI tool categories and computer science concepts - Improve: Flow charts showing decision trees for AI tool adoption #### 🔊 Aural learners - Present: Video format with clear narration and explanations - Improve: Podcast version or audio summary for commute listening #### 🗣️ Verbal learners - Present: Clear definitions of technical terms, analogies to historical tech cycles - Improve: Glossary of AI development terms with context #### 🧩 Kinesthetic learners - Present: Practical advice for monthly AI evaluation sessions - Improve: Hands-on workshop templates for trying AI tools systematically #### 🧠 Logical learners - Present: Structured argument with historical precedents and logical progression - Improve: Decision matrix for evaluating which AI tools to adopt #### 🧑‍🤝‍🧑 Social learners - Present: References to community discussions and expert opinions - Improve: Discussion guides for team conversations about AI adoption strategies #### 🧘‍♀️ Solitary learners - Present: Individual reflection prompts about personal AI tool experiences - Improve: Self-assessment checklist for AI tool effectiveness #### 🌿 Naturalistic learners - Present: Technology evolution analogies and ecosystem thinking - Improve: Metaphors comparing AI tool adoption to natural adaptation processes ### 📊 Accessibility Assessment #### Screen reader semantics - Present: Clear heading structure and logical content flow - Improve: More descriptive alt text for mind map diagrams #### Keyboard navigation - Present: Standard video player controls - Improve: Transcript with navigable timestamps #### Contrast and readability - Present: Good text contrast in video overlay elements - Improve: High contrast mode support for diagrams #### Media and captions - Present: Full transcript provided - Improve: Time-synced captions and chapter markers ### 🌍 DEIB Assessment #### Cultural breadth - Present: Universal developer experiences across different backgrounds - Improve: Examples from diverse programming communities and career paths #### Inclusive language - Present: Gender-neutral terms, accessible technical explanations - Improve: Acknowledge different learning speeds and career stages #### Neurodiversity support - Present: Clear structure with distinct sections and topics - Improve: Content warnings for anxiety-inducing topics about career pressure #### Representation - Present: Cites diverse voices including underrepresented experts - Improve: More varied examples of successful developers with different approaches ### ✅ Logical Validity #### Strong reasoning - Deductive strength: Solid logical progression from premises about learning cycles to conclusions about AI tools - Inductive strength: Good use of historical examples (Mythical Man Month) to support current arguments - Analogical reasoning: Effective parallels between past technology adoption and current AI hype #### Fallacies - Appeal to authority: Over-relies on Karpathy's expertise without examining counter-arguments - False dichotomy: Somewhat presents AI adoption as all-or-nothing rather than acknowledging middle ground approaches #### Tone of voice - Overall tone: Reassuring and pragmatic, using personal experience to counter anxiety-inducing industry narratives ### 🛡️ DARVO Analysis - Deny: No denial patterns present - Attack: No attacks on accusers - Reverse victim/offender: No victim/offender reversals - Emotional cues: Uses calming, reassuring tone to counter industry-induced anxiety ## 🎯 Conclusion The video provides a much-needed counternarrative to the intense pressure developers felt in 2024 to constantly learn new AI tools. The core message is both practical and reassuring: focus on shipping features and solving real problems rather than chasing every new AI workflow or productivity claim. The speaker's advice to limit AI tool exploration to monthly sessions while strengthening fundamental engineering practices offers a sustainable approach to professional development in the AI era. For WordPress developers specifically, this aligns well with the community's pragmatic values and focus on building useful solutions rather than following hype cycles. The key insight that traditional development skills become more valuable, not less valuable, when working with AI provides a foundation for confident career development without constant anxiety about falling behind. ## 🧱 Refactor Appendix ### 📚 Literature Split Pad ### [H1] Focus on shipping over learning every AI tool > [!quote] [feeling behind as a programmer](https://youtu.be/e-_743sQL --- ## Full Transcript [00:00] If I could describe what it was like to [00:02] be a programmer in the year 2025, um, in [00:05] a few words, it would be hype and fear [00:07] of missing out. If you spent any amount [00:10] of time on the internet, you were [00:11] hearing about the latest models, the [00:13] latest tools, how you should be using AI [00:17] to increase your your output and um, [00:20] make your your workflows better. And if [00:23] you're not using these tools or you're [00:25] not staying up to date, then you're [00:27] going to be falling behind. Um, but [00:29] let's let's take a second. Let's take a [00:30] second and look back at this past year [00:32] and just kind of think about, well, are [00:35] you behind? Did did you actually fall [00:37] behind if you didn't stay up to date? [00:39] Um, let me know down in the comments [00:41] like how how are you feeling? Because [00:42] this is a a feeling that a lot of people [00:46] have and have been living with for the [00:49] past year. And I think now that we're [00:53] we're going into a new year, um it's [00:55] time to reflect and also think about how [00:57] do you want to spend 2026? Do you want [01:00] to be dreading every day as a programmer [01:04] or having all of this fear of missing [01:05] out? Um, or do you just want to put your [01:09] nose down and start building and [01:11] shipping features and not worrying about [01:14] what everyone else is doing or where [01:16] what other people are saying is the the [01:19] stuff that you should be paying [01:20] attention to. And really, a lot of this [01:22] came to a head from the tweet by Andre [01:26] Karpathy. And if you're not familiar [01:27] with him, he is actually the person that [01:29] coined the term vibe coding. Um, he's [01:31] worked at OpenAI, he's worked at Tesla. [01:33] if anyone is going to have opinions [01:35] about this that we could potentially [01:37] really make us think, it's going to be [01:39] somebody like Karpathy. Um, and so there [01:41] they open their tweet with, I've never [01:43] felt this much behind as a programmer. [01:45] The profession is being dramatically [01:46] refactored as the bits contributed by [01:48] the programmer increasingly sparse and [01:49] in between. Now, this kind of gets at [01:51] the sentiment that more and more we are [01:54] writing less code. Like AI is writing [01:55] more and more code for us. Now, I don't [01:58] think we're even close to AI writing all [02:00] of the code for us. And as I'll talk [02:02] about, the skill of being an engineer [02:06] and understanding the AI's output, the [02:09] code that it's spitting out is still [02:11] essential and is going to be essential [02:13] for years to come. Like that that skill [02:16] has not gone away. But it could it [02:18] definitely feels like it bec [02:21] tools and some of these late latest [02:22] models. It can feel like magic. It can [02:25] feel like oh my goodness, I'm never [02:26] going to have to write code again. [02:28] >> [snorts] [02:28] >> Um, but as I'm going to talk about th [02:29] those skills are definitely still [02:32] needed. Um, and then he says, "I have a [02:35] sense that I could be 10x more powerful [02:36] if I just properly string together what [02:38] has become available over the last year. [02:40] And a failure to claim the boost feels [02:41] decidedly like a skill issue." [snorts] [02:43] This this is the problem. This is the [02:46] worm that gets into your brain that's [02:47] like, "Oh my goodness, if I just had 10 [02:49] more agents running, I could be [02:51] increasing my output and if I don't, I'm [02:54] falling behind. No, no. Like, if I don't [02:56] figure out how to use these tools or how [02:57] to string them together, I I basically [02:59] I'm I'm I'm an incompetent developer. [03:02] This is the thing that we need to stop [03:04] doing. Obviously, it's it's natural. [03:07] It's it's human to feel this way. But I [03:11] am here to urge you to take a step back, [03:14] take a pause for a second, and realize [03:17] that you don't have to 10x your your [03:20] output. What you're just look at what [03:22] you're doing right now, what you've done [03:23] over the past year. Was it enough? Would [03:25] it have been better if it was 10 times [03:27] more than what you did? Um, I'm I'm [03:30] proud of what I did this past year. So, [03:33] like feeling that you needed to be 10 [03:35] times more productive or that you if you [03:37] don't have 20 agents running in the [03:39] background, you're not doing enough. [03:41] That is the thing that we need to stop [03:44] doing as a whole and just get more [03:47] towards shipping things, right? using [03:49] what we have, what we're good at, and [03:51] building features and shipping and and [03:53] that's really what we need to focus on. [03:55] Now, this next piece that he talks about [03:57] here, I think is actually [03:59] he's laid it out. He's basically told us [04:02] this is all the stuff that you need to [04:04] know if you want to be an agentic [04:06] developer, an AI native developer. Um, [04:11] and it kind of answered his own question [04:14] or his own concerns. So he says there's [04:16] a new programmable layer of of [04:18] abstraction to master in addition to the [04:20] usual layers below involving agents, sub [04:23] aents, their prompts, contacts, [04:24] memories, modes, permissions, tools, [04:26] plug-in skills, hooks, mcb, LSPs, slash [04:28] commands, workflows, ID integrations, [04:29] and a need to build an all-encompassing [04:31] mental model for shrinks and pitfalls of [04:32] fundamentally stocastic, fallible, and [04:34] unintelligible and changing entities [04:35] suddenly intermingled with what used to [04:37] be good old-fashioned engineering. Sure, [04:40] it's a lot. Uh, but we're still early [04:43] days. [laughter] [04:44] And the fact that you can list out all [04:47] of these things means you can learn [04:48] these things. And this is kind of what I [04:50] what I what I want to get to and what my [04:52] main point for all of this is. [04:54] Consider what it took for you, the [04:56] person watching this video, to get to [04:58] where you are in your career. What do [05:01] you have to do? For me, I had to [05:04] initially learn how to learn. This field [05:08] is constantly changing and has always [05:10] been constantly changing. And the best [05:12] of us, the best programmers out there [05:14] are the ones that can adapt and the ones [05:17] that can constantly learn and progress [05:19] while all of this stuff is crazy and [05:21] happening. And when we do it on a daily [05:23] basis, and I personally have been doing [05:25] it for years as well. And so this is no [05:27] different. It feels different because it [05:30] feels like it's moving faster. [05:31] Especially with all of the hype there. [05:33] there's just this is the most hyped [05:34] thing in the history of programming. So [05:37] with all of that it can feel more [05:39] urgent. It can feel like these are [05:40] things that you can't learn or you're [05:42] going to fall behind. But that is not [05:43] the case for that. Let's just let's just [05:46] break this down. Right? He he carbi has [05:48] basically listed out all the things that [05:49] have come out over the past year or so. [05:51] And these are things that are are [05:52] potentially learnable. Right? I I have [05:54] this this nice little mind map here of [05:56] the AI programming layer. And you can [05:58] kind of break it down into six [05:59] categories. You've got orchestration, [06:00] capabilities, integration, models, [06:01] control, and communication. Right? Each [06:03] one of these things you can learn if [06:05] you're technical, if you're a programmer [06:07] already, these are all things that fit [06:10] into to some existing mental model that [06:12] you have and you can learn more about [06:14] them. Do you remember when we were being [06:16] told that if you didn't learn how to [06:18] prompt, you're going to be left behind. [06:20] The ecosystem has essentially evolved to [06:22] a point to where you can type a single [06:24] sentence that's not even grammatically [06:26] correct and AI will still know what you [06:28] want it to do. Right? the models have [06:30] gotten good enough that we don't have to [06:32] know how to prompt as well anymore. Now, [06:34] I'm not saying that this isn't something [06:37] you can work on because you definitely [06:39] can write better prompts. But what I'm [06:41] saying is we were essentially harping on [06:43] this idea of that if you don't practice [06:44] prompting now, you're going to be left [06:46] behind. That is not the case because [06:48] these models have gotten so good that in [06:50] a lot of scenarios, you actually don't [06:52] need detailed prompts to get things done [06:54] and you don't have to word things in a [06:56] specific way. Most of the modern models [06:59] can figure out what you want to do even [07:01] with like the worst worded thing. And [07:02] then you combine that with something [07:04] like plan mode and plan mode can [07:05] basically take your one sentence crappy [07:07] prompt into something that actually AI [07:11] can work with and that is more aligned [07:12] with what you had in mind in your brain [07:14] that you couldn't get into the four [07:16] sentences instead of one sentence. So, [07:18] all of this to say, these concepts, [07:22] while they seem like we need to know [07:24] them all right now, the the ecosystem is [07:26] moving so fast that a lot of this stuff [07:30] won't matter even a year from now. And [07:33] why that's interesting is because when [07:35] the dust settles, when we've essentially [07:37] gotten out of this hype cycle and gotten [07:39] to a point where we know how to [07:42] effectively use these tools, we're going [07:44] to be at a point where we don't have to [07:46] know some of these things. So, we're [07:47] going to be at a point where a lot of [07:48] this stuff is even abstracted away. And [07:51] so, my main bit of guidance here is you [07:55] don't have to have fear of missing out [07:57] and you don't have to worry about [07:59] learning a lot of these things because [08:00] we're going to reach a point where these [08:03] things won't matter anymore. The way [08:05] that we work with AI is going to change. [08:07] So, you could get bogged down in working [08:10] on agents and having the best sub agent [08:12] and and working on your your prompting [08:14] and and your context management, but [08:16] currently those are all still just early [08:18] days. These are not tried and tested [08:21] ways of working with AI. These are still [08:23] experimental. And if you put all of your [08:26] effort into learning these experimental [08:28] things, you're going to feel like things [08:30] are changing constantly because they [08:31] are. So my main bit of guidance is focus [08:35] on shipping features. Focusing focus on [08:38] learning and adapting the things that [08:39] are going to allow you to build faster. [08:41] And it's not everything, right? You [08:43] don't have to be 10x your workflow. You [08:44] don't have to have 20 agents running in [08:46] the background. You should adopt just [08:50] enough AI if it makes sense for you for [08:54] shipping features and building useful [08:57] things and solving real problems. [08:59] Because at the end of the day, that's [09:00] why we do this. That's why we do this [09:02] job is we solve real world problems. [09:04] Most of us anyways [09:06] and we use programming and technology to [09:09] do that. Now another thing I want to [09:12] mention here is so I I've laid this out [09:14] in terms of like six subcategories but [09:16] everything I've learned so far in my [09:18] career has also been broken down in this [09:20] way. Right? So if we look at a mindm map [09:22] of computer science, there's lots of [09:24] subcategories here, each with their own [09:26] intricacies and each thing that you [09:28] could become an expert in. And when I [09:32] was in university, sure, this was maybe [09:33] a little bit overwhelming, but I took it [09:35] one day at a time. And I think we're [09:37] still in a place where you can take this [09:38] one day at a time. And just like uh [09:41] we're seeing this overview of computer [09:42] science here, you can dial into any one [09:44] of these things. And for instance, like [09:46] a subset of programming and really a [09:48] subset of computer science is full stack [09:49] web development. And this is where I [09:50] spend most of my time. But again, this [09:52] can be broken down into a lot of other [09:54] categories like front end and back end [09:55] and databases and infrastructure and [09:56] APIs and security and performance. And [09:58] each one of those things you could be an [09:59] expert in or learn more about. So I [10:01] completely understand yes you could get [10:04] overwhelmed because in my career there [10:06] are a lot of things but at the end of [10:09] the day when I focus on shipping [10:11] features when I focus on just in time [10:13] learning right learning just enough to [10:15] get thing the thing done I don't [10:17] necessarily need to become an expert in [10:19] any one of these areas when I'm working [10:20] on a particular project or a particular [10:22] feature that allows me to keep moving [10:25] and not get bogged down and not have [10:26] fear of missing out or not feeling like [10:28] I need to become an expert in all this [10:29] stuff just to build useful things. So [10:32] really, I just I just want to make that [10:34] parallel for you that even though yes, [10:36] this seems like new stuff and maybe some [10:37] of these words you've never heard of, [10:39] it's just one more thing for you to [10:41] learn. It's just one more thing that's [10:43] similar to all the other things that [10:44] you've already learned. And yes, it's [10:46] different. Yes, it's moving fast, but [10:49] fundamentally it is learnable. Now, I [10:51] want to point out a quote tweet from Adi [10:53] Asmmani, uh, because he has some really [10:54] good points in here, and he kind of [10:57] talks about how what you already know is [11:00] still going to be vital and useful. So, [11:03] in his tweet, he mentions, "The best [11:05] news about this new AI programming layer [11:07] is that traditional engineering skills [11:09] are more valuable than ever, not less. [11:11] It helps us minimize shipping slop." [11:13] Now, there are debates from people on [11:16] whether or not shipping slop is okay. be [11:19] and I I don't even want to get into that [11:21] because there are people basically [11:23] saying don't worry about the code itself [11:25] because eventually AI is going to be so [11:26] good that it can just clean up all the [11:28] slop and it it doesn't even matter. But [11:30] we're so far away from that if that is [11:32] ever going to happen. So you should [11:34] worry about shipping good code even if [11:37] you're using AI to do it. And anybody [11:39] that tells you otherwise doesn't [11:41] understand how all of this stuff works. [11:43] It's literally the Dunning Krueger [11:45] effect in full effect, right? They [11:48] essentially overestimate their abilities [11:50] because they can type a simple prompt [11:52] and AI outputs something for them, but [11:54] they don't know how to validate it. And [11:56] we've seen it time and time again, these [11:59] vibe coders or people that don't [12:00] necessarily understand what AI is [12:02] outputting reach a point where they [12:04] can't move any faster or they can't move [12:06] any further because they have hit their [12:08] wall of understanding and they don't [12:09] even know how to talk to AI. And so this [12:11] is where I can potentially see the [12:13] parallels of prompting or or learning [12:15] how to become a prompt engineer because [12:18] you understand the system well enough [12:20] that you actually can ask AI to fix your [12:22] bug or add a new feature. But at that [12:24] point it's not prompt engineering at [12:26] that point. It's just being a subject [12:28] matter expert. It's just understanding [12:30] complex systems or understanding [12:32] particular programming languages so that [12:34] you can guide an AI to do what is [12:36] needed. And that's not something you can [12:38] learn by just prompting. You have to [12:40] have hands on the keyboard day in dayout [12:43] practice learning these things and [12:45] getting experience with these things [12:46] without AI to be able to guide AI to do [12:49] it later on. And you you could argue [12:51] with me that eventually it won't matter. [12:53] But I I'm not convinced. And on that [12:55] point, Addie talks about developers who [12:57] have already invested in CI/CD, testing, [12:59] documentation, and code review are [13:01] having the most success with AI tools. [13:03] These boring foundations are [13:04] accelerators. They turn agents from [13:06] chaos generators into productivity [13:08] multipliers. Throughout my career, there [13:11] have been waves and cycles of uh [13:13] proponents of things like TDD or [13:15] whenever DevOps became a thing where [13:17] we're talking about CI/CD and and all of [13:19] these various ways to improve your [13:21] workflow and and better ship to [13:22] production. And we've reached a point [13:24] where we have solid workflows for a lot [13:28] of these things. And especially when [13:30] working with AI, this is the best time [13:32] than ever to use all of those things [13:33] because the output from AI is [13:36] unpredictable. Um, is sometimes bad and [13:40] needs to be validated and verified. And [13:43] if you're just shipping AI code all by [13:46] itself and don't have in some sort of [13:48] guard rails around it, you're not going [13:50] to be successful in the long term. And [13:51] so that's what Addy is talking about [13:53] here is if you've already invested in [13:54] these other aspects and as a a software [13:56] developer that cares, I have. I know [13:58] about CI/CD. I know how to write tests. [14:00] I know how to write good documentation. [14:02] I know how to do an effective code [14:04] review. All of these things are only [14:06] going to make working with AI that much [14:08] easier and that much better. And so [14:10] again, you can take all of these [14:11] concepts and blow them out into into a [14:13] mind map, right? And and if you haven't [14:15] done these things, now is a better time [14:17] than ever to learn about these things [14:19] because essentially if you treat AI [14:21] output like just the shittiest [14:23] programmer ever that basically has [14:25] infinite copy paste ability and infinite [14:28] memory recall, that's basically what AI [14:30] output is, right? It's trained on all [14:32] all of this code it's seen and there's a [14:33] lot of bad code that it's been trained [14:35] on and it's going to output bad code. So [14:37] when you start to put these guardrails [14:39] around it, you're going to get better [14:41] output. And so understanding how to use [14:43] these tools which at the end of the day [14:45] are just going to make you a better [14:46] software developer or have a better [14:47] product to begin with regardless of if [14:49] you're using AI or not. These are the [14:51] kinds of things that you should focus [14:52] on. Now I do want to take a step back [14:54] and kind of like bring this all together [14:57] and also kind of talk about this idea of [14:58] agents because this is one of the things [15:00] that most people are harping on right [15:02] now. It's it's the the most hyped thing [15:03] in terms of having uh a swarm of agents [15:06] or multiple background agents. that is [15:08] you have all these little one-off AIs [15:10] that are all working on your codebase at [15:11] the same time. And all I can think about [15:15] is this idea from the mythical man [15:16] month. So this is a book that was [15:18] published in 1975 where its central [15:20] theme is adding manpower to a software [15:23] project that is behind schedule delays [15:25] it even longer. So 50 years ago, we were [15:28] already talking about this idea that if [15:30] you throw more port programmers at the [15:32] problem, it's not going to solve it any [15:33] faster. [snorts] And we are essentially [15:36] making that mistake again with agents. [15:38] Now, understanding how paralyzable your [15:41] tasks are is an effective skill set, but [15:45] that's something that you need to work [15:46] on. You can't immediately just throw a [15:47] bunch of agents at your codebase and [15:49] expect it all to work out, right? [15:50] Because you're going to fall into the [15:51] same traps that every single business [15:54] has fallen into where they just hire [15:55] more programmers or throw more [15:56] programmers at the problem. And at the [15:58] end of the day, if you don't understand [16:00] the problems you're trying to solve or [16:01] you haven't effectively analyzed how it [16:03] can be broken down into subtasks, you're [16:06] not going to get any more efficiency out [16:08] of that. And so having that FOMO of [16:10] being a 10x engineer and then launching [16:12] 30 agents isn't going to get you any [16:14] closer. You have to focus on the [16:17] fundamentals. Focus on actually [16:20] understanding how things can be [16:22] parallelized and how things can be [16:23] broken down. Now, with all of that, I [16:25] want to leave you with some wise words [16:27] from uh Sir Dax. And uh if you're if [16:30] you're looking for some level-headed [16:32] takes on AI, definitely give Dax a [16:34] follow. But in response to Carpathy's [16:37] tweet, he says, "There's some truth to [16:39] this, but the most common use of all [16:41] these new knobs and levers is [16:42] procrastination." And this is also what [16:44] I'm getting at. You can spend all of [16:45] your time trying to learn the latest [16:46] tools and techniques, but at the end of [16:48] the day, are you shipping features? Are [16:50] you getting things done? Are you [16:51] providing value? Are you solving [16:53] problems? And for a lot of you, you're [16:55] probably not. You're probably spending [16:56] so much time just tweaking your workflow [16:58] to get the perfect AI output and the [17:01] perfect amount of agent orchestration, [17:03] but it's all just procrastination. Are [17:05] you shipping features? And to get back [17:07] to what I was talking about earlier of, [17:09] okay, there's all this stuff that [17:10] Carpety talked about. Do you need to [17:12] know all of this? I don't think so. And [17:14] so, I don't think you should spend all [17:15] of your cycles learning absolutely all [17:17] of this. I think you should pick and [17:18] choose what you find to be most [17:20] effective. But at the end of the day, a [17:23] lot of this stuff is going to sit a lot [17:24] lower in the stack. So what DAX says is [17:25] the abstractions being developed right [17:26] now around agents are going to sit low [17:28] in the stack. I don't feel very good [17:30] about them. It's been such little time [17:32] and there have been already been so many [17:33] avoidable mistakes. There's even a pride [17:35] around the lack of effort with we just [17:37] prompted it. So this idea that all of [17:41] these different workflows with agents [17:43] and prompts and and context engineering, [17:45] we are still trying to figure out the [17:47] best ways to work with AI. and we [17:49] haven't found it yet. Because if we [17:50] found it, we would absolutely all be out [17:52] of a job. If we found it, we're [17:54] basically at the singularity. We're [17:56] basically at a point where AI is writing [17:58] absolutely everything and we don't need [17:59] humans at all. But we're not there and [18:00] we're not even close to being there. So [18:04] with that said, a lot of this stuff [18:06] eventually you're not even going to have [18:07] to worry about. A lot of this stuff you [18:09] won't even need to understand because [18:12] it'll be abstracted away. And that's [18:14] completely fine. But the things that [18:16] really matter are are the fundamentals [18:18] or the things that are that are keep [18:20] that that have always been the case. And [18:22] that's really what what you should be [18:24] focusing on. And lastly, I'll I'll leave [18:25] you with this one. Dax says, "I've said [18:28] forever that it takes years to evaluate [18:29] any new tech. Have to get past your [18:31] initial excitement bias. See what [18:32] unforeseen issues pop up. Understand how [18:34] it scales with complexity. This 100% [18:36] applies to coding with AI as well. We [18:39] have seen so much hype over the past [18:41] year and at the end of the day we're all [18:43] just really excited um and a lot of [18:47] issues have popped up and we still [18:48] haven't settled on the best way to work [18:51] with AI and things are constantly [18:52] changing. So my advice for you is remove [18:58] yourself from [laughter] the hype, ship [19:00] features, do what you do best, and if [19:04] you do want to, you know, try to stay up [19:07] to date, because at the end of the day, [19:08] I'm not saying bury your head in the [19:10] sand. I'm not saying that you shouldn't [19:11] be aware of some of the new models or [19:14] the new ways of working with AI, but [19:17] what I am saying is you shouldn't spend [19:18] all of your time on it because there are [19:20] better things to be spending your time [19:22] on, right? So my advice for you going [19:24] into the new year is maybe set aside one [19:28] day a month or two days a month [19:30] [laughter] [19:31] where you sit down and take a look at [19:34] what are the latest models that have [19:35] been released. What are some new [19:36] workflows that people are talking about? [19:37] What is actually working, right? And [19:40] maybe you you set aside a day where you [19:42] try those things out. But don't spend [19:43] every day doing it. Don't get to the [19:46] point where most of your time is just [19:48] configuring and and trying to make find [19:50] the the the best flow here because that [19:53] is taking away from the actual time [19:55] spent building and shipping and solving [19:58] real problems.  ## 📺 Video ![](https://www.youtube.com/watch?v=e-_743sQLa8) --- # 📝 Full Notes (Deep-Read Analysis) # 🏷️ Title: feeling behind as a programmer ## 🧰 Action Inbox - [H1] Focus on shipping features over learning every AI tool — stop FOMO-driven development #productivity - [H2] Set aside 1-2 days per month for AI tool evaluation — avoid daily hype consumption #time-management - [H3] Strengthen CI/CD, testing, and code review practices — these amplify AI benefits #fundamentals ## 🧭 Table of Contents - [📝 Summary](#-summary) - [📖 Main Content](#-main-content) - [🗒️ Notes](#️-notes) - [📚 References](#-references) - [🗣️ Paraphrase](#️-paraphrase) - [📰 Newsletter](#-newsletter) - [✅ Action Items](#-action-items) - [🧪 Hypotheses](#-hypotheses) - [📊 Methodology](#-methodology) - [❓ Questions](#-questions) - [🔮 Future Research](#-future-research) - [💡 Implications](#-implications) - [🧠 Learning Styles Assessment](#-learning-styles-assessment) - [📊 Accessibility Assessment](#-accessibility-assessment) - [🌍 DEIB Assessment](#-deib-assessment) - [✅ Logical Validity](#-logical-validity) - [🛡️ DARVO Analysis](#-darvo-analysis) - [🎯 Conclusion](#-conclusion) ### 🔗 References - [André Karpathy's Tweet](https://twitter.com/karpathy) - [The Mythical Man Month](https://en.wikipedia.org/wiki/The_Mythical_Man-Month) - [Dax's Response](https://twitter.com/DavidKPiano) ## 📝 Summary - A developer reflects on the intense FOMO and pressure to constantly learn new AI tools that dominated 2024 - André Karpathy's viral tweet about feeling "behind as a programmer" sparked widespread anxiety in the developer community - The core message: focus on shipping features and solving real problems rather than chasing every new AI tool or workflow - Traditional engineering skills (CI/CD, testing, documentation) are more valuable than ever when working with AI - Most experimental AI workflows will be abstracted away as the ecosystem matures ## 📖 Main Content The video addresses the widespread anxiety among developers in 2024 about falling behind due to rapidly evolving AI tools. The creator responds to André Karpathy's influential tweet expressing feelings of being "dramatically behind" as a programmer, where Karpathy listed numerous new AI concepts developers feel pressured to master. The speaker argues this represents a classic case of FOMO (fear of missing out) that's counterproductive. They emphasize that core programming fundamentals remain essential - AI tools still require human expertise to validate output, understand complex systems, and guide implementations effectively. The video draws parallels to historical technology cycles, noting that today's experimental AI workflows will likely be abstracted away as the ecosystem matures. The creator advocates for "just enough" AI adoption focused on shipping features rather than pursuing 10x productivity claims through agent orchestration. Key supporting arguments include references to "The Mythical Man Month" principles about why adding more resources (or agents) doesn't automatically solve problems faster, and observations that developers with strong CI/CD and testing practices are seeing the most success with AI tools. ## 🗒️ Notes #### ⏱️ [00:00](https://youtu.be/e-_743sQLa8?t=0) — [H1] The programming climate of 2024: hype and FOMO - Key idea: Developer culture dominated by pressure to constantly learn new AI tools - Evidence: "If I could describe what it was like to be a programmer in the year 2025, it would be hype and fear of missing out" #### ⏱️ [01:30](https://youtu.be/e-_743sQLa8?t=90) — [H2] André Karpathy's viral confession about feeling behind - Key idea: Even AI experts feel overwhelmed by the pace of change - Evidence: "I've never felt this much behind as a programmer. The profession is being dramatically refactored" #### ⏱️ [02:15](https://youtu.be/e-_743sQLa8?t=135) — [H3] The dangerous 10x productivity mindset - Key idea: Belief that not using all available AI tools represents incompetence - Evidence: "I could be 10x more powerful if I just properly string together what has become available... failure to claim the boost feels decidedly like a skill issue" #### ⏱️ [04:00](https://youtu.be/e-_743sQLa8?t=240) — [H2] Cataloging the new AI programming layer - Key idea: The new concepts are numerous but ultimately learnable - Evidence: Lists agents, sub-agents, prompts, contexts, memories, modes, permissions, tools as examples #### ⏱️ [06:30](https://youtu.be/e-_743sQLa8?t=390) — [H2] Learning has always been core to programming - Key idea: Adapting to new technologies is a fundamental developer skill - Evidence: "The best programmers are the ones that can adapt and constantly learn and progress" #### ⏱️ [07:45](https://youtu.be/e-_743sQLa8?t=465) — [H3] The prompting skills myth debunked - Key idea: Models have improved enough that detailed prompting isn't critical - Evidence: "You can type a single sentence that's not even grammatically correct and AI will still know what you want it to do" #### ⏱️ [09:20](https://youtu.be/e-_743sQLa8?t=560) — [H2] Most experimental tools won't matter long-term - Key idea: The ecosystem is moving so fast that current workflows will be obsoleted - Evidence: "When the dust settles... we're going to reach a point where these things won't matter anymore" #### ⏱️ [11:00](https://youtu.be/e-_743sQLa8?t=660) — [H1] Focus on shipping features, not tool mastery - Key idea: Core developer purpose remains solving real-world problems - Evidence: "Focus on just enough AI if it makes sense for shipping features and building useful things" #### ⏱️ [13:15](https://youtu.be/e-_743sQLa8?t=795) — [H2] Traditional engineering skills are more valuable than ever - Key idea: Fundamental development practices amplify AI tool effectiveness - Evidence: "Developers who have already invested in CI/CD, testing, documentation, and code review are having the most success" #### ⏱️ [15:30](https://youtu.be/e-_743sQLa8?t=930) — [H3] AI output requires validation like bad programmer code - Key idea: Treating AI as an unreliable programmer with infinite copy-paste ability - Evidence: "It's trained on a lot of bad code and it's going to output bad code" #### ⏱️ [16:45](https://youtu.be/e-_743sQLa8?t=1005) — [H2] The agent swarm fallacy - Key idea: Adding more agents doesn't solve problems faster, echoing Mythical Man Month - Evidence: "Adding manpower to a software project that is behind schedule delays it even longer" #### ⏱️ [18:20](https://youtu.be/e-_743sQLa8?t=1100) — [H3] Most AI workflow optimization is procrastination - Key idea: Perfecti ng AI setups often replaces actual productive work - Evidence: "The most common use of all these new knobs and levers is procrastination" #### ⏱️ [19:30](https://youtu.be/e-_743sQLa8?t=1170) — [H2] We haven't found the best AI workflows yet - Key idea: Current approaches are experimental and will evolve significantly - Evidence: "If we found it, we would absolutely all be out of a job... we're not there and we're not even close" #### ⏱️ [20:45](https://youtu.be/e-_743sQLa8?t=1245) — [H1] Practical advice: limit AI tool exploration to monthly sessions - Key idea: Structured approach to staying current without constant distraction - Evidence: "Set aside one day a month or two days a month where you take a look at what are the latest models" ## 📚 References - [André Karpathy Tweet Thread](https://twitter.com/karpathy) - [The Mythical Man Month by Frederick Brooks](https://en.wikipedia.org/wiki/The_Mythical_Man-Month) - [Dax's Twitter Response](https://twitter.com/DavidKPiano) ## 🗣️ Paraphrase - Developers in 2024 experienced unprecedented pressure to constantly learn new AI tools, creating anxiety about falling behind professionally - The fundamental skills of programming - understanding systems, validating code, breaking down problems - remain essential even when using AI assistance - Rather than pursuing maximum productivity through tool optimization, developers should focus on shipping features and solving real problems with appropriate AI integration ## 📰 Newsletter **Fighting Developer FOMO in the AI Era** The programming world spent 2024 in a state of constant anxiety about AI tools. This thoughtful reflection offers a much-needed reality check for WordPress developers feeling overwhelmed by the pace of change. **Key insight**: Your existing skills in testing, CI/CD, and code review are more valuable than ever when working with AI. These practices act as essential guardrails for validating AI-generated code. **Practical takeaway**: Instead of daily tool-chasing, set aside 1-2 days per month to evaluate new AI developments. Focus the rest of your time on what matters most - shipping features that solve real problems. The WordPress ecosystem has always valued pragmatic approaches over hype cycles. This same principle applies beautifully to AI adoption. Resources: [Learn WordPress](https://learn.wordpress.org/), [Developer Docs](https://developer.wordpress.org/), [WP-CLI](https://wp-cli.org/) ## ✅ Action Items #### DevRel - Developer advocacy: Create content addressing AI tool FOMO in developer communities - Education: Develop workshops on effective AI integration without workflow disruption - Developer experience: Document best practices for AI-assisted development workflows - Community management: Moderate discussions around AI tool adoption with balanced perspectives #### WordPress - Core: Evaluate AI tools for core development while maintaining code quality standards - Documentation: Create guidance on when and how to use AI for WordPress development - Community: Address AI anxiety in WordCamp talks and meetup discussions - Training: Develop learning paths that balance AI awareness with fundamental skills ## 🧪 Hypotheses - Developers who focus on shipping features while selectively adopting AI tools will outperform those constantly switching workflows - Organizations with strong CI/CD and testing practices will see better ROI from AI tool adoption than those without - The current experimental AI workflows will be largely obsoleted within 2-3 years by improved abstractions - Monthly rather than daily AI tool evaluation produces better long-term outcomes for developer productivity ### 📊 Methodology - Survey developers on time spent learning vs. shipping to test productivity hypothesis - Compare AI adoption success rates between teams with/without established engineering practices - Track longevity of current AI workflow tools to validate abstraction timeline ### ❓ Questions - How much AI tool learning time is optimal for maintaining competitiveness without hampering productivity? - Which fundamental programming skills will become more valuable as AI capabilities increase? - What indicators can help developers distinguish between lasting AI innovations and temporary hype? ### 🔮 Future Research - Long-term studies on developer career outcomes based on AI adoption strategies - Analysis of which 2024 AI tools actually persist and gain widespread adoption - Investigation of optimal team structures for human-AI collaboration in software development ### 💡 Implications - Practice: Shift focus from tool mastery to selective AI integration based on shipping needs - Education: Emphasize fundamental engineering skills as AI amplifiers rather than replacements - Career: Develop strategies for staying current without falling into constant learning traps - Team management: Balance AI experimentation time with feature delivery commitments ### 🧠 Learning Styles Assessment Reading level estimate: 9th-10th grade #### 👀 Visual learners - Present: Mind maps showing AI tool categories and computer science concepts - Improve: Flow charts showing decision trees for AI tool adoption #### 🔊 Aural learners - Present: Video format with clear narration and explanations - Improve: Podcast version or audio summary for commute listening #### 🗣️ Verbal learners - Present: Clear definitions of technical terms, analogies to historical tech cycles - Improve: Glossary of AI development terms with context #### 🧩 Kinesthetic learners - Present: Practical advice for monthly AI evaluation sessions - Improve: Hands-on workshop templates for trying AI tools systematically #### 🧠 Logical learners - Present: Structured argument with historical precedents and logical progression - Improve: Decision matrix for evaluating which AI tools to adopt #### 🧑‍🤝‍🧑 Social learners - Present: References to community discussions and expert opinions - Improve: Discussion guides for team conversations about AI adoption strategies #### 🧘‍♀️ Solitary learners - Present: Individual reflection prompts about personal AI tool experiences - Improve: Self-assessment checklist for AI tool effectiveness #### 🌿 Naturalistic learners - Present: Technology evolution analogies and ecosystem thinking - Improve: Metaphors comparing AI tool adoption to natural adaptation processes ### 📊 Accessibility Assessment #### Screen reader semantics - Present: Clear heading structure and logical content flow - Improve: More descriptive alt text for mind map diagrams #### Keyboard navigation - Present: Standard video player controls - Improve: Transcript with navigable timestamps #### Contrast and readability - Present: Good text contrast in video overlay elements - Improve: High contrast mode support for diagrams #### Media and captions - Present: Full transcript provided - Improve: Time-synced captions and chapter markers ### 🌍 DEIB Assessment #### Cultural breadth - Present: Universal developer experiences across different backgrounds - Improve: Examples from diverse programming communities and career paths #### Inclusive language - Present: Gender-neutral terms, accessible technical explanations - Improve: Acknowledge different learning speeds and career stages #### Neurodiversity support - Present: Clear structure with distinct sections and topics - Improve: Content warnings for anxiety-inducing topics about career pressure #### Representation - Present: Cites diverse voices including underrepresented experts - Improve: More varied examples of successful developers with different approaches ### ✅ Logical Validity #### Strong reasoning - Deductive strength: Solid logical progression from premises about learning cycles to conclusions about AI tools - Inductive strength: Good use of historical examples (Mythical Man Month) to support current arguments - Analogical reasoning: Effective parallels between past technology adoption and current AI hype #### Fallacies - Appeal to authority: Over-relies on Karpathy's expertise without examining counter-arguments - False dichotomy: Somewhat presents AI adoption as all-or-nothing rather than acknowledging middle ground approaches #### Tone of voice - Overall tone: Reassuring and pragmatic, using personal experience to counter anxiety-inducing industry narratives ### 🛡️ DARVO Analysis - Deny: No denial patterns present - Attack: No attacks on accusers - Reverse victim/offender: No victim/offender reversals - Emotional cues: Uses calming, reassuring tone to counter industry-induced anxiety ## 🎯 Conclusion The video provides a much-needed counternarrative to the intense pressure developers felt in 2024 to constantly learn new AI tools. The core message is both practical and reassuring: focus on shipping features and solving real problems rather than chasing every new AI workflow or productivity claim. The speaker's advice to limit AI tool exploration to monthly sessions while strengthening fundamental engineering practices offers a sustainable approach to professional development in the AI era. For WordPress developers specifically, this aligns well with the community's pragmatic values and focus on building useful solutions rather than following hype cycles. The key insight that traditional development skills become more valuable, not less valuable, when working with AI provides a foundation for confident career development without constant anxiety about falling behind. ## 🧱 Refactor Appendix ### 📚 Literature Split Pad ### [H1] Focus on shipping over learning every AI tool > [!quote] [feeling behind as a programmer](https://youtu.be/e-_743sQL --- # 📄 Full Transcript *Auto-generated from YouTube captions · timestamps roughly every 30 seconds · [watch on YouTube](https://www.youtube.com/watch?v=e-_743sQLa8)* **[00:00]** If I could describe what it was like to be a programmer in the year 2025, um, in a few words, it would be hype and fear of missing out. If you spent any amount of time on the internet, you were hearing about the latest models, the latest tools, how you should be using AI to increase your your output and um, make your your workflows better. And if you're not using these tools or you're not staying up to date, then you're going to be falling behind. Um, but let's let's take a second. Let's take a **[00:30]** second and look back at this past year and just kind of think about, well, are you behind? Did did you actually fall behind if you didn't stay up to date? Um, let me know down in the comments like how how are you feeling? Because this is a a feeling that a lot of people have and have been living with for the past year. And I think now that we're we're going into a new year, um it's time to reflect and also think about how do you want to spend 2026? Do you want to be dreading every day as a programmer **[01:04]** or having all of this fear of missing out? Um, or do you just want to put your nose down and start building and shipping features and not worrying about what everyone else is doing or where what other people are saying is the the stuff that you should be paying attention to. And really, a lot of this came to a head from the tweet by Andre Karpathy. And if you're not familiar with him, he is actually the person that coined the term vibe coding. Um, he's worked at OpenAI, he's worked at Tesla. if anyone is going to have opinions **[01:35]** about this that we could potentially really make us think, it's going to be somebody like Karpathy. Um, and so there they open their tweet with, I've never felt this much behind as a programmer. The profession is being dramatically refactored as the bits contributed by the programmer increasingly sparse and in between. Now, this kind of gets at the sentiment that more and more we are writing less code. Like AI is writing more and more code for us. Now, I don't think we're even close to AI writing all of the code for us. And as I'll talk about, the skill of being an engineer **[02:06]** and understanding the AI's output, the code that it's spitting out is still essential and is going to be essential for years to come. Like that that skill has not gone away. But it could it definitely feels like it bec tools and some of these late latest models. It can feel like magic. It can feel like oh my goodness, I'm never going to have to write code again. >> [snorts] >> Um, but as I'm going to talk about th those skills are definitely still needed. Um, and then he says, "I have a sense that I could be 10x more powerful **[02:36]** if I just properly string together what has become available over the last year. And a failure to claim the boost feels decidedly like a skill issue." [snorts] This this is the problem. This is the worm that gets into your brain that's like, "Oh my goodness, if I just had 10 more agents running, I could be increasing my output and if I don't, I'm falling behind. No, no. Like, if I don't figure out how to use these tools or how to string them together, I I basically I'm I'm I'm an incompetent developer. This is the thing that we need to stop doing. Obviously, it's it's natural. **[03:07]** It's it's human to feel this way. But I am here to urge you to take a step back, take a pause for a second, and realize that you don't have to 10x your your output. What you're just look at what you're doing right now, what you've done over the past year. Was it enough? Would it have been better if it was 10 times more than what you did? Um, I'm I'm proud of what I did this past year. So, like feeling that you needed to be 10 times more productive or that you if you **[03:37]** don't have 20 agents running in the background, you're not doing enough. That is the thing that we need to stop doing as a whole and just get more towards shipping things, right? using what we have, what we're good at, and building features and shipping and and that's really what we need to focus on. Now, this next piece that he talks about here, I think is actually he's laid it out. He's basically told us this is all the stuff that you need to know if you want to be an agentic developer, an AI native developer. Um, **[04:11]** and it kind of answered his own question or his own concerns. So he says there's a new programmable layer of of abstraction to master in addition to the usual layers below involving agents, sub aents, their prompts, contacts, memories, modes, permissions, tools, plug-in skills, hooks, mcb, LSPs, slash commands, workflows, ID integrations, and a need to build an all-encompassing mental model for shrinks and pitfalls of fundamentally stocastic, fallible, and unintelligible and changing entities suddenly intermingled with what used to be good old-fashioned engineering. Sure, it's a lot. Uh, but we're still early **[04:43]** days. [laughter] And the fact that you can list out all of these things means you can learn these things. And this is kind of what I what I what I want to get to and what my main point for all of this is. Consider what it took for you, the person watching this video, to get to where you are in your career. What do you have to do? For me, I had to initially learn how to learn. This field is constantly changing and has always been constantly changing. And the best of us, the best programmers out there **[05:14]** are the ones that can adapt and the ones that can constantly learn and progress while all of this stuff is crazy and happening. And when we do it on a daily basis, and I personally have been doing it for years as well. And so this is no different. It feels different because it feels like it's moving faster. Especially with all of the hype there. there's just this is the most hyped thing in the history of programming. So with all of that it can feel more urgent. It can feel like these are things that you can't learn or you're going to fall behind. But that is not the case for that. Let's just let's just **[05:46]** break this down. Right? He he carbi has basically listed out all the things that have come out over the past year or so. And these are things that are are potentially learnable. Right? I I have this this nice little mind map here of the AI programming layer. And you can kind of break it down into six categories. You've got orchestration, capabilities, integration, models, control, and communication. Right? Each one of these things you can learn if you're technical, if you're a programmer already, these are all things that fit into to some existing mental model that you have and you can learn more about them. Do you remember when we were being told that if you didn't learn how to **[06:18]** prompt, you're going to be left behind. The ecosystem has essentially evolved to a point to where you can type a single sentence that's not even grammatically correct and AI will still know what you want it to do. Right? the models have gotten good enough that we don't have to know how to prompt as well anymore. Now, I'm not saying that this isn't something you can work on because you definitely can write better prompts. But what I'm saying is we were essentially harping on this idea of that if you don't practice prompting now, you're going to be left behind. That is not the case because **[06:48]** these models have gotten so good that in a lot of scenarios, you actually don't need detailed prompts to get things done and you don't have to word things in a specific way. Most of the modern models can figure out what you want to do even with like the worst worded thing. And then you combine that with something like plan mode and plan mode can basically take your one sentence crappy prompt into something that actually AI can work with and that is more aligned with what you had in mind in your brain that you couldn't get into the four sentences instead of one sentence. So, all of this to say, these concepts, **[07:22]** while they seem like we need to know them all right now, the the ecosystem is moving so fast that a lot of this stuff won't matter even a year from now. And why that's interesting is because when the dust settles, when we've essentially gotten out of this hype cycle and gotten to a point where we know how to effectively use these tools, we're going to be at a point where we don't have to know some of these things. So, we're going to be at a point where a lot of this stuff is even abstracted away. And so, my main bit of guidance here is you **[07:55]** don't have to have fear of missing out and you don't have to worry about learning a lot of these things because we're going to reach a point where these things won't matter anymore. The way that we work with AI is going to change. So, you could get bogged down in working on agents and having the best sub agent and and working on your your prompting and and your context management, but currently those are all still just early days. These are not tried and tested ways of working with AI. These are still experimental. And if you put all of your **[08:26]** effort into learning these experimental things, you're going to feel like things are changing constantly because they are. So my main bit of guidance is focus on shipping features. Focusing focus on learning and adapting the things that are going to allow you to build faster. And it's not everything, right? You don't have to be 10x your workflow. You don't have to have 20 agents running in the background. You should adopt just enough AI if it makes sense for you for shipping features and building useful **[08:57]** things and solving real problems. Because at the end of the day, that's why we do this. That's why we do this job is we solve real world problems. Most of us anyways and we use programming and technology to do that. Now another thing I want to mention here is so I I've laid this out in terms of like six subcategories but everything I've learned so far in my career has also been broken down in this way. Right? So if we look at a mindm map of computer science, there's lots of subcategories here, each with their own intricacies and each thing that you **[09:28]** could become an expert in. And when I was in university, sure, this was maybe a little bit overwhelming, but I took it one day at a time. And I think we're still in a place where you can take this one day at a time. And just like uh we're seeing this overview of computer science here, you can dial into any one of these things. And for instance, like a subset of programming and really a subset of computer science is full stack web development. And this is where I spend most of my time. But again, this can be broken down into a lot of other categories like front end and back end and databases and infrastructure and APIs and security and performance. And each one of those things you could be an **[09:59]** expert in or learn more about. So I completely understand yes you could get overwhelmed because in my career there are a lot of things but at the end of the day when I focus on shipping features when I focus on just in time learning right learning just enough to get thing the thing done I don't necessarily need to become an expert in any one of these areas when I'm working on a particular project or a particular feature that allows me to keep moving and not get bogged down and not have fear of missing out or not feeling like I need to become an expert in all this **[10:29]** stuff just to build useful things. So really, I just I just want to make that parallel for you that even though yes, this seems like new stuff and maybe some of these words you've never heard of, it's just one more thing for you to learn. It's just one more thing that's similar to all the other things that you've already learned. And yes, it's different. Yes, it's moving fast, but fundamentally it is learnable. Now, I want to point out a quote tweet from Adi Asmmani, uh, because he has some really good points in here, and he kind of talks about how what you already know is **[11:00]** still going to be vital and useful. So, in his tweet, he mentions, "The best news about this new AI programming layer is that traditional engineering skills are more valuable than ever, not less. It helps us minimize shipping slop." Now, there are debates from people on whether or not shipping slop is okay. be and I I don't even want to get into that because there are people basically saying don't worry about the code itself because eventually AI is going to be so good that it can just clean up all the slop and it it doesn't even matter. But we're so far away from that if that is **[11:32]** ever going to happen. So you should worry about shipping good code even if you're using AI to do it. And anybody that tells you otherwise doesn't understand how all of this stuff works. It's literally the Dunning Krueger effect in full effect, right? They essentially overestimate their abilities because they can type a simple prompt and AI outputs something for them, but they don't know how to validate it. And we've seen it time and time again, these vibe coders or people that don't necessarily understand what AI is outputting reach a point where they **[12:04]** can't move any faster or they can't move any further because they have hit their wall of understanding and they don't even know how to talk to AI. And so this is where I can potentially see the parallels of prompting or or learning how to become a prompt engineer because you understand the system well enough that you actually can ask AI to fix your bug or add a new feature. But at that point it's not prompt engineering at that point. It's just being a subject matter expert. It's just understanding complex systems or understanding particular programming languages so that **[12:34]** you can guide an AI to do what is needed. And that's not something you can learn by just prompting. You have to have hands on the keyboard day in dayout practice learning these things and getting experience with these things without AI to be able to guide AI to do it later on. And you you could argue with me that eventually it won't matter. But I I'm not convinced. And on that point, Addie talks about developers who have already invested in CI/CD, testing, documentation, and code review are having the most success with AI tools. These boring foundations are **[13:04]** accelerators. They turn agents from chaos generators into productivity multipliers. Throughout my career, there have been waves and cycles of uh proponents of things like TDD or whenever DevOps became a thing where we're talking about CI/CD and and all of these various ways to improve your workflow and and better ship to production. And we've reached a point where we have solid workflows for a lot of these things. And especially when working with AI, this is the best time than ever to use all of those things because the output from AI is **[13:36]** unpredictable. Um, is sometimes bad and needs to be validated and verified. And if you're just shipping AI code all by itself and don't have in some sort of guard rails around it, you're not going to be successful in the long term. And so that's what Addy is talking about here is if you've already invested in these other aspects and as a a software developer that cares, I have. I know about CI/CD. I know how to write tests. I know how to write good documentation. I know how to do an effective code review. All of these things are only going to make working with AI that much **[14:08]** easier and that much better. And so again, you can take all of these concepts and blow them out into into a mind map, right? And and if you haven't done these things, now is a better time than ever to learn about these things because essentially if you treat AI output like just the shittiest programmer ever that basically has infinite copy paste ability and infinite memory recall, that's basically what AI output is, right? It's trained on all all of this code it's seen and there's a lot of bad code that it's been trained on and it's going to output bad code. So when you start to put these guardrails **[14:39]** around it, you're going to get better output. And so understanding how to use these tools which at the end of the day are just going to make you a better software developer or have a better product to begin with regardless of if you're using AI or not. These are the kinds of things that you should focus on. Now I do want to take a step back and kind of like bring this all together and also kind of talk about this idea of agents because this is one of the things that most people are harping on right now. It's it's the the most hyped thing in terms of having uh a swarm of agents or multiple background agents. that is you have all these little one-off AIs **[15:10]** that are all working on your codebase at the same time. And all I can think about is this idea from the mythical man month. So this is a book that was published in 1975 where its central theme is adding manpower to a software project that is behind schedule delays it even longer. So 50 years ago, we were already talking about this idea that if you throw more port programmers at the problem, it's not going to solve it any faster. [snorts] And we are essentially making that mistake again with agents. Now, understanding how paralyzable your **[15:41]** tasks are is an effective skill set, but that's something that you need to work on. You can't immediately just throw a bunch of agents at your codebase and expect it all to work out, right? Because you're going to fall into the same traps that every single business has fallen into where they just hire more programmers or throw more programmers at the problem. And at the end of the day, if you don't understand the problems you're trying to solve or you haven't effectively analyzed how it can be broken down into subtasks, you're not going to get any more efficiency out of that. And so having that FOMO of being a 10x engineer and then launching **[16:12]** 30 agents isn't going to get you any closer. You have to focus on the fundamentals. Focus on actually understanding how things can be parallelized and how things can be broken down. Now, with all of that, I want to leave you with some wise words from uh Sir Dax. And uh if you're if you're looking for some level-headed takes on AI, definitely give Dax a follow. But in response to Carpathy's tweet, he says, "There's some truth to this, but the most common use of all these new knobs and levers is procrastination." And this is also what **[16:44]** I'm getting at. You can spend all of your time trying to learn the latest tools and techniques, but at the end of the day, are you shipping features? Are you getting things done? Are you providing value? Are you solving problems? And for a lot of you, you're probably not. You're probably spending so much time just tweaking your workflow to get the perfect AI output and the perfect amount of agent orchestration, but it's all just procrastination. Are you shipping features? And to get back to what I was talking about earlier of, okay, there's all this stuff that Carpety talked about. Do you need to know all of this? I don't think so. And so, I don't think you should spend all **[17:15]** of your cycles learning absolutely all of this. I think you should pick and choose what you find to be most effective. But at the end of the day, a lot of this stuff is going to sit a lot lower in the stack. So what DAX says is the abstractions being developed right now around agents are going to sit low in the stack. I don't feel very good about them. It's been such little time and there have been already been so many avoidable mistakes. There's even a pride around the lack of effort with we just prompted it. So this idea that all of these different workflows with agents and prompts and and context engineering, we are still trying to figure out the **[17:47]** best ways to work with AI. and we haven't found it yet. Because if we found it, we would absolutely all be out of a job. If we found it, we're basically at the singularity. We're basically at a point where AI is writing absolutely everything and we don't need humans at all. But we're not there and we're not even close to being there. So with that said, a lot of this stuff eventually you're not even going to have to worry about. A lot of this stuff you won't even need to understand because it'll be abstracted away. And that's completely fine. But the things that really matter are are the fundamentals **[18:18]** or the things that are that are keep that that have always been the case. And that's really what what you should be focusing on. And lastly, I'll I'll leave you with this one. Dax says, "I've said forever that it takes years to evaluate any new tech. Have to get past your initial excitement bias. See what unforeseen issues pop up. Understand how it scales with complexity. This 100% applies to coding with AI as well. We have seen so much hype over the past year and at the end of the day we're all just really excited um and a lot of issues have popped up and we still **[18:48]** haven't settled on the best way to work with AI and things are constantly changing. So my advice for you is remove yourself from [laughter] the hype, ship features, do what you do best, and if you do want to, you know, try to stay up to date, because at the end of the day, I'm not saying bury your head in the sand. I'm not saying that you shouldn't be aware of some of the new models or the new ways of working with AI, but what I am saying is you shouldn't spend all of your time on it because there are **[19:20]** better things to be spending your time on, right? So my advice for you going into the new year is maybe set aside one day a month or two days a month [laughter] where you sit down and take a look at what are the latest models that have been released. What are some new workflows that people are talking about? What is actually working, right? And maybe you you set aside a day where you try those things out. But don't spend every day doing it. Don't get to the point where most of your time is just configuring and and trying to make find the the the best flow here because that **[19:53]** is taking away from the actual time spent building and shipping and solving real problems.