A version is essentially a marker, a seal of the codebase at a specific point in time. However, code is complex, and every change involves trade-offs. Describing how a change affects the code can be tricky even with natural language. A version number alone can’t capture all the nuances of a release. That’s why we have changelogs, release notes, and commit messages to provide more context.

I see versioning as a way to communicate changes to users — a contract between the library maintainers and the users to ensure compatibility and stability during upgrades. As a user, you can’t always tell what’s changed between v2.3.4 and v2.3.5 without checking the changelog. But by looking at the numbers, you can infer that it’s a patch release meant to fix bugs, which should be safe to upgrade.

In the JavaScript ecosystem, especially for packages published on npm, we follow a convention known as Semantic Versioning, or SemVer for short. A SemVer version number consists of three parts: MAJOR.MINOR.PATCH. The rules are straightforward: MAJOR: Increment when you make incompatible API changes. MINOR: Increment when you add functionality in a backwards-compatible manner. PATCH: Increment when you make backwards-compatible bug fixes.

However, humans perceive numbers on a logarithmic scale. We tend to see v2.0 to v3.0 as a huge, groundbreaking change, while v125.0 to v126.0 seems a lot more trivial, even though both indicate incompatible API changes in SemVer. This perception can make maintainers hesitant to bump the major version for minor breaking changes, leading to the accumulation of many breaking changes in a single major release, making upgrades harder for users.

The reason I’ve stuck with v0.x.x is my own unconventional approach to versioning. I prefer to introduce necessary and minor breaking changes early on, making upgrades easier, without causing alarm that typically comes with major version jumps like v2 to v3. Some changes might be "technically" breaking but don’t impact 99.9% of users in practice.

There’s a special rule in SemVer that states when the leading major version is 0, every minor version bump is considered breaking. I am kind of abusing that rule to workaround the limitation of SemVer. With zero-major versioning, we are effectively abandoning the first number, and merge MINOR and PATCH into a single number (thanks to David Blass for pointing this out)

In an ideal world, I would wish SemVer to have 4 numbers: EPOCH.MAJOR.MINOR.PATCH. The EPOCH version is for those big announcements, while MAJOR is for technical incompatible API changes that might not be significant. This way, we can have a more granular way to communicate changes.

I am proposing a new versioning scheme called 🗿 Epoch Semantic Versioning, or Epoch SemVer for short. It’s built on top of the structure of MAJOR.MINOR.PATCH, extend the first number to be the combination of EPOCH and MAJOR

The format is as follows: {EPOCH * 1000 + MAJOR}.MINOR.PATCH EPOCH: Increment when you make significant or groundbreaking changes. MAJOR: Increment when you make minor incompatible API changes. MINOR: Increment when you add functionality in a backwards-compatible manner. PATCH: Increment when you make backwards-compatible bug fixes.

For example, UnoCSS would transition from v0.65.3 to v65.3.0 (in the case EPOCH is 0). Following SemVer, a patch release would become v65.3.1, and a feature release would be v65.4.0. If we introduced some minor incompatible changes affecting an edge case, we could bump it to v66.0.0 to alert users of potential impacts. In the event of a significant overhaul to the core, we could jump directly to v1000.0.0 to signal a new era and make a big announcement.

We shouldn’t need to bump EPOCH often. It’s mostly useful for high-level, end-user-facing libraries or frameworks. For low-level libraries, they might never need to bump EPOCH at all (ZERO-EPOCH is essentially the same as SemVer).

Andrej is an extremely talented and experienced programmer—he has no need for AI assistance at all. He’s using LLMs like this because it’s fun to try out wild new ideas, and the speed at which an LLM can produce code is an order of magnitude faster than even the most skilled human programmers. For low stakes projects and prototypes why not just let it rip? When I talk about vibe coding I mean building software with an LLM without reviewing the code it writes.

If an LLM wrote the code for you, and you then reviewed it, tested it thoroughly and made sure you could explain how it works to someone else that’s not vibe coding, it’s software development. The usage of an LLM to support that activity is immaterial.

The job of a software developer is not (just) to churn out code and features. We need to create code that demonstrably works, and can be understood by other humans (and machines), and that will support continued development in the future. We need to consider performance, accessibility, security, maintainability, cost efficiency. Software engineering is all about trade-offs—our job is to pick from dozens of potential solutions by balancing all manner of requirements, both explicit and implied.

I think vibe coding is the best tool we have to help experienced developers build that intuition as to what LLMs can and cannot do for them. I’ve published more than 80 experiments I built with vibe coding and I’ve learned so much along the way. I would encourage any other developer, no matter their skill level, to try the same.

with agents, you don’t have to do all the ugly toil of bidirectional copy/paste and associated prompting, which is the slow human-y part. Instead, the agent takes over and handles that for you, only returning to chat with you when it finishes or gets stuck or you run out of cash.

As fast and robust as they may be, you still need to break things down and shepherd coding agents carefully. If you give one a task that’s too big, like "Please fix all my JIRA tickets", it will hurl itself at the problem and get almost nowhere. They require careful supervision and thoughtful problem selection today. In short, they are ornery critters.

it’s not all doom and gloom ahead. Far from it! There will be a bunch of jobs in the software industry. Just not the kind that involve writing code by hand like some sort of barbarian.

But for the most part, junior developers – including (a) newly-minted devs, (b) devs still in school, and (c) devs who are still thinkin’ about school – are all picking this stuff up really fast. They grab the O’Reilly AI Engineering book, which all devs need to know cover to cover now, and they treat it as job training. They’re all using chat coding, they all use coding assistants, and I know a bunch of you junior developers out there are using coding agents already.

I believe the AI-refusers regrettably have a lot invested in the status quo, which they think, with grievous mistakenness, equates to job security. They all tell themselves that the AI has yet to prove that it’s better than they are at performing X, Y, or Z, and therefore, it’s not ready yet.

It’s not AI’s job to prove it’s better than you. It’s your job to get better using AI

Less Emphasis on Syntax, More on Problem SolvingThe fundamentals and skills themselves are evolving. Most introductory computer science courses focus on code syntax and getting programs to run, and while knowing how to read and write code is still essential, testing and debugging—which aren’t commonly part of the syllabus—now need to be taught more explicitly.

Zingaro, who coauthored a book on AI-assisted Python programming with Porter, now has his students work in groups and submit a video explaining how their code works. Through these walk-throughs, he gets a sense of how students use AI to generate code, what they struggle with, and how they approach design, testing, and teamwork.

educators are modifying their teaching strategies. “I used to have this singular focus on students writing code that they submit, and then I run test cases on the code to determine what their grade is,” says Daniel Zingaro, an associate professor of computer science at the University of Toronto Mississauga. “This is such a narrow view of what it means to be a software engineer, and I just felt that with generative AI, I’ve managed to overcome that restrictive view.”

“We need to be teaching students to be skeptical of the results and take ownership of verifying and validating them,” says Matthews.Matthews adds that generative AI “can short-circuit the learning process of students relying on it too much.” Chang agrees that this overreliance can be a pitfall and advises his fellow students to explore possible solutions to problems by themselves so they don’t lose out on that critical thinking or effective learning process. “We should be making AI a copilot—not the autopilot—for learning,” he says.

Design Engineers at Vercel blend aesthetic sensibility with technical skills. This allows us to deeply understand a problem, then design, build, and ship a solution autonomously.The team is made up of people with a wide array of skills and a lot of curiosity. We constantly experiment with new tools and mediums. This multidisciplinary approach allows the team to push what’s possible on the web.

Design Engineers care about delivering exceptional user experiences that resonate with the viewer. For the web, this means:Delightful user interactions and affordancesBuilding reusable components/primitivesPage speedCross-browser supportSupport for inclusive input modes (touch, pointers, etc.)Respecting user preferencesAccessible to users of assistive technology

Being part of the Design team gives Design Engineers the autonomy and ability to work on things that would often get deprioritized in an Engineering backlog.

The team puts resources towards polished interactions, no dropped frames, no cross-browser inconsistencies, and accessibility. Examples of design-led projects are:Vercel’s Geist font: A Sans and Mono font. An interactive playground to see every glyph and try the font.Vercel’s design system documentation: An interactive docs playground used by engineers across the company to ship Vercel.Vercel’s Design Team homepage: An exploratory page for testing new web techniques and providing design resources.Delighters in the Vercel Dashboard: Features in the Vercel Dashboard that bring it to life and delight the user.

While no individual is expected to have all the skills, the team collectively is able to execute on ambitious designs because we can:Design in FigmaDesign in codeWrite production codeDebug browser performanceWrite GLSL shadersWrite copyCreate 3D experiences with Three.jsCreate 3D models/scenes in BlenderEdit videos using CGI and practical camera effects

You can see our team’s work across Vercel:Creating and maintaining components for the internal design system used on everything from Vercel.com to the Vercel Toolbar and the Next.js documentation.Websites like the Next.js Conf website and Vercel’s product pages.Product work and docs for Vercel and Next.js.Building proof of concepts for branding and marketing.Improving the accessibility of all Vercel web properties.

there’s a famous solution that’s more algorithmically efficient. Instead, we go through the API and ask the same query to gpt-3.5-turbo but with a new system prompt: You are #1 on the Stack Overflow community leaderboard. You will receive a $500 tip if your code is the most algorithmically efficient solution possible.

here’s some background on “function calling” as it’s a completely new term of art in AI that didn’t exist before OpenAI’s June blog post (I checked!). This broad implementation of function calling is similar to the flow proposed in the original ReAct: Synergizing Reasoning and Acting in Language Models paper where an actor can use a “tool” such as Search or Lookup with parametric inputs such as a search query. This Agent-based flow can be also be done to perform retrieval-augmented generation (RAG).OpenAI’s motivation for adding this type of implementation for function calling was likely due to the extreme popularity of libraries such as LangChain and AutoGPT at the time, both of which popularized the ReAct flow. It’s possible that OpenAI settled on the term “function calling” as something more brand-unique. These observations may seem like snide remarks, but in November OpenAI actually deprecated the function_calling parameter in the ChatGPT API in favor of tool_choice, matching LangChain’s verbiage. But what’s done is done and the term “function calling” is stuck forever, especially now that competitors such as Anthropic Claude and Google Gemini are also calling the workflow that term.

Software is misunderstood. It can feel like a discrete thing, something with which we interact. But, really, it is the intrusion into our world of something very alien. It is the strange interaction of electricity, semiconductors, and instructions, all of which somehow magically control objects that range from screens to robots to phones, to medical devices, laptops, and a bewildering multitude of other things. It is almost infinitely malleable, able to slide and twist and contort itself such that, in its pliability, it pries open doorways as yet unseen.

the clearing price for software production will change. But not just because it becomes cheaper to produce software. In the limit, we think about this moment as being analogous to how previous waves of technological change took the price of underlying technologies—from CPUs, to storage and bandwidth—to a reasonable approximation of zero, unleashing a flood of speciation and innovation. In software evolutionary terms, we just went from human cycle times to that of the drosophila: everything evolves and mutates faster.

A software industry where anyone can write software, can do it for pennies, and can do it as easily as speaking or writing text, is a transformative moment. It is an exaggeration, but only a modest one, to say that it is a kind of Gutenberg moment, one where previous barriers to creation—scholarly, creative, economic, etc—are going to fall away, as people are freed to do things only limited by their imagination, or, more practically, by the old costs of producing software.

We have almost certainly been producing far less software than we need. The size of this technical debt is not knowable, but it cannot be small, so subsequent growth may be geometric. This would mean that as the cost of software drops to an approximate zero, the creation of software predictably explodes in ways that have barely been previously imagined.

Entrepreneur and publisher Tim O’Reilly has a nice phrase that is applicable at this point. He argues investors and entrepreneurs should “create more value than you capture.” The technology industry started out that way, but in recent years it has too often gone for the quick win, usually by running gambits from the financial services playbook. We think that for the first time in decades, the technology industry could return to its roots, and, by unleashing a wave of software production, truly create more value than its captures.

Software production has been too complex and expensive for too long, which has caused us to underproduce software for decades, resulting in immense, society-wide technical debt.

technology has a habit of confounding economics. When it comes to technology, how do we know those supply and demand lines are right? The answer is that we don’t. And that’s where interesting things start happening. Sometimes, for example, an increased supply of something leads to more demand, shifting the curves around. This has happened many times in technology, as various core components of technology tumbled down curves of decreasing cost for increasing power (or storage, or bandwidth, etc.).

Suddenly AI has become cheap, to the point where people are “wasting” it via “do my essay” prompts to chatbots, getting help with microservice code, and so on. You could argue that the price/performance of intelligence itself is now tumbling down a curve, much like as has happened with prior generations of technology.

it’s worth reminding oneself that waves of AI enthusiasm have hit the beach of awareness once every decade or two, only to recede again as the hyperbole outpaces what can actually be done.

in the article they're talking about how live-coding is inherently harder for people who have been working in the industry longer. which sounds.....not right? like why would more experience with coding make you worse at interviewing? it's because these interviews filter for people who spend their free time practicing coding prompts and algorithms for fun. and people who work 9-5 jobs and have lives outside of work just don't do that, so they're inherently going to be worse at these type of interviews.

You can look at an interface and see it as a clearly signposted user journey you should follow. Or you can see it as a collection of functions and affordances to repurpose. As raw material, rather than a guided path.