1,749 mentions · 790 chunks · 131 episodes
People who can code via LLMs don't necessarily have an intuition for what plain old code can do. That leads to, for example, trying to create an Anthropic Artifact to identify wh...
Artifacts were a low-hanging fruit made possible by LLMs, just waiting to be discovered. How Anthropic Built Artifacts: https://newsletter.pragmaticengineer.com/p/how-anthropic-built-artifacts The feature w...
...being too precious about it. That was extremely hard to do automatically before LLMs. You had to significantly cut down on the things that could be done, to the subset that could be made tinkerable. But LLMs are squishy! They can squi...
...it was told to do, no judgment. Cheap to execute! But now software can also use LLMs in its execution. LLMs are like magic in software. Software with an LLM inside is squishy, alive, emergent but also a bit unpredictable. An LLM can u...
LLMs scramble the cost equation of software. Before, software was expensive to write, cheap to run. But now LLMs make software much cheaper to write. At l...
Software written by LLMs is merely good, not great. If it's small and similar to existing software, it's typically good enough. But if it's larger, or unlike existing softwar...
A metaphor for LLMs: an electric bicycle for the mind. Bicycles are about extending human agency but you're very much still steering. If you already know how to bike, yo...
...iding hand of a human, the more often it will produce these turds. That's where LLMs that can give diffs inline while you're working are more helpful. The human and the LLM can iterate together continuously, instead of the LLM going o...
...nd value of the product gets higher... automatically! You could for example use LLMs to set the static floor of quality (a close-ended component) and then add an ecosystem component on top that compounds in quality with more usage (an...
LLMs are so charismatic, you can talk to them like a human. So every AI tool puts them front and center, even though in most of the cases you want them to...
Why do engineers have such a hard time working with LLMs? Because we're using engineering metaphors to describe a fundamentally squishy thing that is better described by organic or biological metaphors. The...
...st times computers can't extract that context. But humans can get the vibe, and LLMs can too. If an LLM can give a collection of data a good title, that shows that the context established is clear. Each incremental step of work should...
...ut that lack of structure will bite you later if you try to do anything scaled. LLMs can do all kinds of fuzzy structured things. For example, take a picture of the books on your bookshelf and ask for a JSON representation, most LLMs ...
...t people used to building traditional software are having trouble incorporating LLMs. Traditional software does exactly what you tell it to (which might not be what you meant). You can design it precisely and pin it to the wall and it...
A pattern we see in LLMs: linear improvement in quality for exponential increases in costs. (Of course, over time we've also rapidly improved the efficiency to deliver previo...
...s expensive. It requires an expensive, specialist human. Running code is cheap. LLMs are more expensive than normal code, but can write bespoke code that can be run cheaply.
... techniques like folksonomies. What if you could have LLM-assisted JIT schemas? LLMs interpreting things like OpenAPI specs on two sides and writing bespoke translation code. A massive number of services today document themselves with...
...ive accomplishment and benefit... but also leaky, hard to reason about cleanly. LLMs can maintain a quite large context window to patiently sift through. Code written for LLMs will have less abstraction, less leverage. Code written by...
People are launching platforms for building things with LLMs faster than people are building useful LLM-native apps. As an industry we learned the "in a gold rush sell pickaxes" lesson, and now everyone is doin...
... AI-native your application is: Could it have ever plausibly been viable before LLMs if you had enough capital?