What I believe to be one of the most underrated features of LLMs is the ability to convert imprecise human text into code.

In the past, writing a computer program required writing code - aka writing text in a very specific, tedious way such that it can be understood by a computer. A single erroneous character will crash an entire program. Writing code is difficult and tedious enough that it is a professional career that tends to require some degree of study (eg. 4 year CS degree, coding bootcamp), and the job can command annual compensation packages from $100k-$1m+/yr.

LLMs for the first time enable writing programs through natural language, ie. without writing code.

What this will do is make it dramatically easier to create computer programs. The technological jump is the equivalent of trying to talk to someone in Icelandic in 1989 (assuming you don't speak Icelandic), and then suddenly being given access to a modern smartphone with Google Translate.

Some implications:

- Anyone will be able to easily create or update computer programs
- Computer programs will easily be able to create computer programs
- Coding for the first time in history will just be done by regular human language instructions that any non-technical person can write. A disgruntled customer or product manager will simply be able to create a bug report or feature request, and an AI agent will immediately be able to take that and update the codebase (eg. submitting a pull request after testing and code reviewing its own work)
- Programming languages will become unimportant, because any language will easily be able to be translated into any other
- Coding will be commoditized

The human programmer is the translation layer between the human and the machine, and AI is automating that work away, just like it's automating human language translators.

*AI skeptic: So what? Who cares if some geeks can make computer programs easier?*

AI and automation are computer programs!

**AI is making it easier to improve AI.**

This is a feedback loop that results in exponential growth of AI. The more AI improves, the more AI is able to improve itself. Eventually this feedback loop gets so out of control that humans cannot keep up, a point known as the "singularity".

Humans are terrible at comprehending exponential growth, and thus I believe are drastically underestimating how quickly this is all developing.

Most seem to think that the job market for most people is going to be fine over the next decade - I vehemently disagree. I believe that AI will dramatically change society over the next few years and make the workforce drastically more competitive as AI starts to outstrip human ability. The labor market will become so competitive that UBI will become inevitable (I've been saying this for a decade, but never before has it looked so obvious and imminent).

The nature of work will move to a higher level of abstraction. Workers will become more like managers who delegate the "grunt work" to AI - responsible less for the nitty gritty of implementation and building tedious things, and responsible more for deciding what to build and where to direct the AI agents' focus. Just like fast mental math skills hasn't really been a competitive advantage since the invention of the calculator, doing any sort of repetitive, automatable work will not be of any advantage because the AI will be able to do it faster and cheaper at a speed of gigabytes/s, while being able to self-correct and debug itself. As tech becomes commoditized, humans will have to focus more on the "human" aspect such as marketing and zero sum games to economically compete.

The mistake that people who dismiss this tend to make is that they often believe that AGI is required for this mass societal transformation. I disagree. All it takes is for computer programs to be able to make the creation of computer programs easier to achieve this AI flywheel effect.

Anyways just some random thoughts here at 2:11am in Seoul.

LLMs create an Exponential Flywheel for AI Advancement

Hi I'm Jeremy. I'm building this site because I'm not satisfied with other online community type websites and their lack of innovation (see the /about page). The goal is to make a superior, more open, and more decentralized online community.

The site is still in early stages in active development (see the dev masterthread here: https://zsync.xyz/p/zsync-dev-update-masterthread)

Join us if this sounds interesting! Feel free to send me any feedback or reach out directly!

HN: <https://news.ycombinator.com/item?id=37301991>

WebLLM: Llama2 in the Browser

It's clear that we're approaching the singularity, & most are drastically underestimating the impact that AI will have on society within the next couple years. Some predictions:

\- Humans will not be writing code by hand in a few years.

\- Workers will be expected to move "up the stack" of abstraction. Software engineers will be expected to be more high level product manager hybrids, serving more as technical business managers who delegate low level tasks to AI agents + cheap offshore workers

\- Rise in generalists. Expensive specialists are most at risk since AI democratizes knowledge + power

\- Gutting of the middle class, since most peoples' jobs are now very easy to automate

\- Rise in AI agents. Most of what you read online including on internet forums and in DMs, you won't be able to tell if it was written by a human or AI (already the case with recruiting). There is currently an arms race in AI agent development, the results of which we'll start seeing on society within the next 12 months.

\- The glory days of working in tech are over. Just like very other industry, it's only going to get more and more competitive. Salaries will at best remain stagnant. This is not specific to tech and will be the case for every industry. The industries most resilient will be those that require a human (eg. physical therapy)

The winners will be those who harness AI to their advantage, the losers will be everyone else.

I think most people are drastically underestimating the enormous impact that AI will have on society within even just the next 12 months. People are treating AI like a temporary fad or bubble, as if things will just return to the way they were before. But AI is not only here to stay, it's just getting started, and it's an arms race because everyone knows that AI will eat everything. Humans are terrible at understanding exponential growth, and most cannot comprehend how fast things are about to change.

Not trying to make this political - but I don't see any easier method of alleviating the hemorrhaging of the middle class than governments implementing a Universal Basic Income (UBI). Humans just cannot compete with a superintelligent autonomous AI that has parsed every book and document on the internet. We can continue to be in denial of this while our citizens suffer and overdose suicide to cope with the depression and bleak opportunities, or we can embrace AI and leverage it to free people to spend their time at their own leisure, moving society collectively up Maslow's Hierarchy of Needs. But given how blind and reactionary most people are to this kind of stuff, I don't predict anything changes until we reach a serious boiling point, so unfortunately billions will needlessly suffer in the meantime until the masses wake up to the cosmic societal shifts that are taking place as we speak.

<https://twitter.com/jeremybernier/status/1695350480388714605>

Implications of Approaching the Singularity

HN: <https://news.ycombinator.com/item?id=37250821>

Build a chatbot with custom data sources, powered by LlamaIndex

HN: <https://news.ycombinator.com/item?id=37148210>

LlamaGPT – Self-hosted, offline, private AI chatbot, powered by Llama 2

MetaGPT takes a one line requirement as input and outputs user stories / competitive analysis / requirements / data structures / APIs / documents, etc.

Internally, MetaGPT includes product managers / architects / project managers / engineers. It provides the entire process of a software company along with carefully orchestrated SOPs.

MetaGPT - Given one line Requirement, return PRD, Design, Tasks, Repo

Sweep is an open-source AI-powered junior developer. You describe a feature or bugfix in a GitHub issue and Sweep writes a pull request with code.

Demo Video: <https://www.youtube.com/watch?v=WBVna_ow8vo>

Sweep (YC S23) – A bot to create simple PRs in your codebase 

Meta Open Sources AudioCraft: Generative AI for Audio

99% of dev work has already been done before. That's why most modern dev is "Search Google" -&gt; StackOverflow or random blog post -&gt; copy & paste + tweak code

LLMs and AI code completion tools (eg. GitHub Copilot) drastically simplify this even further. In a few years, you'll simply write a prompt and the code will be written for you, infrastructure set up for you, and APIs glued together for you.

The takeaway is that basic app development will be completely commoditized within a few years, and ultimately all dev work will be. Once something has been done once and is understood by AI, then that work is now commoditized.

Developers are the "translator" between someone who wants to build something and the machine. Just like AI will automate human language translators, AI will do the same for machine translators. Of course there will still be humans verifying the AI's output and handling edge cases, but there will be significantly less demand for humans.

For developers to remain employable, they'll need to offer more than commoditized dev services. This means working at the bleeding edge of what hasn't already been done before (eg. building the AI), and/or coupling dev with other skills (eg. product, marketing, management).

Ultimately though it's an arms race where every worker is competing against their irrelevance due to automation, and it's only going to get more and more competitive as AI improves exponentially.

The tech job market sucks now, but I personally don't think it's going to get better. If/when we get back to low interest rates and another market bubble we might see another boom cycle, but long-term things are only going to get more competitive, and the longevity and time relevance of your knowledge will only decrease as tech advances exponentially.

Of course this needn't be a bad thing if our economic system was designed to accommodate automation such as via a Universal Basic Income (UBI) that ensured that job automation doesn't equate to billions of people becoming destitute, but unfortunately such obvious simple policies are beyond the cognitive capacity of politicians, and everyone else is too focused on the arms race of trying to remain economically relevant in the face of AI automation to pay attention to the bigger picture. Or even if most people see the writing on the wall and have identified the solution, actually implementing the solution is a prisoner's dilemma that requires mass collaboration and individually doesn't help you due to the time/energy opportunity cost (your skills are getting outdated as you read this) and it going against the elite's interests as poorer masses = more elite power (eg. imagine how many less OnlyFans girls there'd be if college tuition wasn't so expensive and we had a UBI).

Basically UBI would be the easiest solution to countering the massive job losses that will result from automation, while redesigning our system to have automation work FOR us rather than AGAINST us (since automation then shifts from "oh no I might lose my job" to "cool I don't need to do this menial work anymore thanks to technology"). But supporting UBI means you're lazy and want to be a useless parasite who just collects a paycheck and plays videogames right?

Speaking of which, I need to get back to work before I lose my economic relevance.

<https://twitter.com/jeremybernier/status/1686573852770664449>

We're Competing With Each Other Against Our Own Economic Irrelevance

Show HN: <https://news.ycombinator.com/item?id=36958731>

Magic Loops – Combine LLMs and code to create simple automations

Chidori – Declarative framework for AI agents (Rust, Python, and Node.js)

"Hi HN, we’re Nate and Ty, co-founders of Continue, an open-source autopilot for software development built to be deeply customizable and continuously learn from development data. It consists of an extended language server and (to start) a VS Code extension.

Our GitHub is <https://github.com/continuedev/continue>. You can watch a demo of Continue and download the extension at <https://continue.dev>

— — —

A growing number of developers are replacing Google + Stack Overflow with Large Language Models (LLMs) as their primary approach to get help, similar to how developers previously replaced reference manuals with Google + Stack Overflow.

However, existing LLM developer tools are cumbersome black boxes. Developers are stuck copy/pasting from ChatGPT and guessing what context Copilot uses to make a suggestion. As we use these products, we expose how we build software and give implicit feedback that is used to improve their LLMs, yet we don’t benefit from this data nor get to keep it.

The solution is to give developers what they need: *transparency, hackability,* and *control*. Every one of us should be able to reason about what’s going on, tinker, and have control over our own development data. This is why we created Continue.

— — —

At its most basic, Continue removes the need for copy/pasting from ChatGPT—instead, you collect context by highlighting and then ask questions in the sidebar or have an edit streamed directly to your editor.

But Continue also provides powerful tools for managing context. For example, type ‘@issue’ to quickly reference a GitHub issue as you are prompting the LLM, ‘@[README.md](http://README.md)’ to reference such a file, or ‘@google’ to include the results of a Google search.

And there’s a ton of room for further customization. Today, you can write your own

\- slash commands (e.g. ‘/commit’ to write a summary and commit message for staged changes, ‘/docs’ to grab the contents of a file and update documentation pages that depend on it, ‘/ticket’ to generate a full-featured ticket with relevant files and high-level instructions from a short description)

\- context sources (e.g. GitHub issues, Jira, local files, StackOverflow, documentation pages)

\- templated system message (e.g. “Always give maximally concise answers. Adhere to the following style guide whenever writing code: {{ /Users/nate/repo/[styleguide.md](http://styleguide.md) }}”)

\- tools (e.g. add a file, run unit tests, build and watch for errors)

\- policies (e.g. define a goal-oriented agent that works in a write code, run code, read errors, fix code, repeat loop)

Continue works with any LLM, including local models using ggml or open-source models hosted on your own cloud infrastructure, allowing you to remain 100% private. While OpenAI and Anthropic perform best today, we are excited to support the progress of open-source as it catches up (<https://continue.dev/docs/customization#change-the-default-l>[...](https://continue.dev/docs/customization#change-the-default-llm)).

When you use Continue, you automatically collect data on how you build software. By default, this development data is saved to `.continue/dev_data` on your local machine. When combined with the code that you ultimately commit, it can be used to improve the LLM that you or your team use (if you allow).

You can read more about how development data is generated as a byproduct of LLM-aided development and why we believe that you should start collecting it now: <https://medium.com/@continuedev/its-time-to-collect-data-on->[...](https://medium.com/@continuedev/its-time-to-collect-data-on-how-you-build-software-197d12a020d5)

Continue has an Apache 2.0 license. We plan to make money by offering organizations a paid development data engine—a continuous feedback loop that ensures the LLMs always have fresh information and code in their preferred style.

— — —

We’d love for you to try out Continue and give us feedback! Let us know what you think in the comments : )"

Show HN: Continue – Open-source coding autopilot

AI will take more jobs from women than men by 2030, report says

Working in tech has always required constantly staying up to date. The rate of progress increases exponentially, meaning this rate of catchup required increases over time. Basically there's no long-term stability in tech. On the bright side, this makes it easier for newcomers and people who have the time + energy to enter the industry (eg. I imagine most who profited off the AI craze didn't have an AI background) 

This phenomenon will start affecting every industry including ones that used to be considered stable like law + medicine. No one is safe.

My advice to anyone in tech is to not get married to any one framework, language, or part of the stack. Assume it will be obsolete in a few years. Learn the 80% needed to be productive, but that extra 20% required for mastering that specific tool is prob not worth it given churn.

I personally think we're going to see the rise of generalists. Jobs will become more managerial, delegating tasks to AI + cheap offshore labor. Companies and especially startups will hire less employees and they'll be expected to be able to handle any task, leveraging AI to learn. LLMs like ChatGPT make it drastically easier to get up to speed on anything, and AI code autocomplete tools like GitHub Copilot lesson the need for knowing specific syntax. Eventually coding will just be writing in plan English (Copilot can already do this, it's just very slow now), as well as everything else (eg. managing infrastructure). Eventually one will just say "create a cron job that scrapes this API every X minutes, filters the results on these keywords, stores the filtered results in a database, and sends me an email notification" and the whole thing will be created. The tech for this is already possible.

Job stability will continue to diminish as tech advances, something tech workers are already familiar with

TypeChat uses the type definitions in your codebase to retrieve structured AI responses that are type-safe.

TypeChat

The AI boom is screwing over Gen Z | ChatGPT is commandeering the mundane tasks that young employees have relied on to advance their careers.

[Fable](https://venturebeat.com/games/fables-the-simulation-lets-you-build-a-society-of-ais-based-on-character-nfts/), a San Francisco startup running a big project dubbed [The Simulation](https://venturebeat.com/games/the-simulation-by-fable-makes-everyone-robert-ford/), has released Showrunner AI technology, dubbed SHOW-1, which can generate new episodes of TV shows

Paper: <https://fablestudio.github.io/showrunner-agents/>

AI-Generated South Park Episodes (by Fable)

"AutoChain takes inspiration from LangChain and AutoGPT and aims to solve both problems by providing a lightweight and extensible framework for developers to build their own agents using LLMs with custom tools and [automatically evaluating](https://github.com/Forethought-Technologies/AutoChain#workflow-evaluation) different user scenarios with simulated conversations. Experienced user of LangChain would find AutoChain is easy to navigate since they share similar but simpler concepts."

AutoChain, lightweight and testable alternative to LangChain

AI Emerges as a Common Enemy for Actors and Writers in Hollywood

Solid thread by Sam Hogan <https://twitter.com/0xSamHogan>

6 months ago it looked like AI / LLMs were going to bring a much needed revival to the venture startup ecosystem after a few tough years. 

With companies like Jasper starting to slow down, it’s looking like this may not be the case. 

Right now there are 2 clear winners, a handful of losers, and a small group of moonshots that seem promising. 

Let’s start with the losers. 

Companies like Jasper and the VCs that back them are the biggest losers right now. Jasper raised &gt;$100M at a 10-figure valuation for what is essentially a generic, thin wrapper around OpenAI. Their UX and brand are good, but not great, and competition from companies building differentiated products specifically for high-value niches are making it very hard to grow with such a generic product. I’m not sure how this pans out but VC’s will likely lose their money. 

The other category of losers are the VC-backed teams building at the application layer that raised $250K-25M in Dec - March on the back of the chatbot craze with the expectation that they would be able to sell to later-stage and enterprise companies. These startups typically have products that are more focused than something very generic like Jasper, but still don't have a real technology moat; the products are easy to copy. 

Executives at enterprise companies are excited about AI, and have been vocal about this from the beginning. This led a lot of founders and VC's to believe these companies would make good first customers. What the startups building for these companies failed to realize is just how aligned and savvy executives and the engineers they manage would be at quickly getting AI into production using open-source tools. An engineering leader would rather spin up their own 
@LangChainAI
 and 
@trychroma
 infrastructure for free and build tech themselves than buy something from a new, unproven startup (and maybe pick up a promotion along the way). 

In short, large companies are opting to write their own AI success stories rather than being a part of the growth metrics a new AI startup needs to raise their next round. 

(This is part of an ongoing shift in the way technology is adopted; I'll discuss this in a post next week.)

This brings us to our first group of winners — established companies and market incumbents. Most of them had little trouble adding AI into their products or hacking together some sort of "chat-your-docs" application internally for employee use. This came as a surprise to me. Most of these companies seemed to be asleep at the wheel for years. They somehow woke up and have been able to successfully navigate the LLM craze with ample dexterity. 

There are two causes for this:
1\. Getting AI right is a life or death proposition for many of these companies and their executives; failure here would mean a slow death over the next several years. They can't risk putting their future in the hands of a new startup that could fail and would rather lead projects internally to make absolutely sure things go as intended.
2\. There is a certain amount of kick-ass wafting through halls of the C-Suite right now. Ambitious projects are being green-lit and supported in ways they weren't a few years ago. I think we owe this in part to 
@elonmusk
 reminding us of what is possible when a small group of smart people are highly motivated to get things done. Reduce red-tape, increase personal responsibility, and watch the magic happen.

Our second group of winners live on the opposite side of this spectrum; indie devs and solopreneurs. These small, often one-man outfits do not raise outside capital or build big teams. They're advantage is their small size and ability to move very quickly with low overhead. They build niche products for niche markets, which they often dominate. The goal is build a saas product (or multiple) that generates \~$10k/mo in relatively passive income. This is sometimes called "mirco-saas."

These are the 
@levelsio
's and 
@dannypostmaa
's of the world. They are part software devs, part content marketers, and full-time modern internet businessmen. They answer to no one except the markets and their own intuition.

This is the biggest group of winners right now. Unconstrained by the need for a $1B+ exit or the goal of $100MM ARR, they build and launch products in rapid-fire fashion, iterating until PMF and cashflow, and moving on to the next. They ruthlessly shutdown products that are not performing. 

LLMs and text-to-image models a la Stable Diffusion have been a boon for these entrepreneurs, and I personally know of dozens of successful (keeping in mind their definition of successful) apps that were started less than 6 months ago. The lifestyle and freedom these endeavors afford to those that perform well is also quite enticing. 

I think we will continue to see the number of successful micro-saas AI apps grow in the next 12 months. This could possibly become one of the biggest cohorts creating real value with this technology. 

The last group I want to talk about are the AI Moonshots — companies that are fundamentally re-imagining an entire industry from the ground up. Generally, these companies are VC-backed and building products that have the potential to redefine how a small group of highly-skilled humans interact with and are assisted by technology. It's too early to tell if they'll be successful or not; early prototypes have been compelling. This is certainly the most exciting segment to watch. 

A few companies I would put in this group are:
1\. https://cursor.so - an AI-first code editor that could very well change how software is written.
2\. https://harvey.ai - AI for legal practices
3\.  https://runwayml.com - an AI-powered video editor

This is an incomplete list, but overall I think the Moonshot category needs to grow massively if we're going to see the AI-powered future we've all been hoping for.

If you're a founder in the $250K-25M raised category and are having a hard time finding PMF for your chatbot or LLMOps company, it may be time to consider pivoting to something more ambitious.

Lets recap:
1\. VC-backed companies are having a hard time. The more money a company raised, the more pain they're feeling.
2\. Incumbents and market leaders are quickly become adept at deploying cutting-edge AI using internal teams and open-source, off-the-shelf technology, cutting out what seemed to be good opportunities for VC-backed startups.
3\. Indie devs are building small, cash-flowing businesses by quickly shipping niche AI-powered products in niche markets.
4\. A small number of promising Moonshot companies with unproven technology hold the most potential for VC-sized returns.

It's still early. This landscape will continue to change as new foundational models are released and toolchains improve. I'm sure you can find counter examples to everything I've written about here. Put them in the comments for others to see.

And just to be upfront about this, I fall squarely into the "raised $250K-25M without PMF" category. If you're a founder in the same boat, I'd love to talk. My DMs are open.

If you enjoyed this post, don't forget to follow me, Sam Hogan. I share one long-form post per week covering AI, startups, open-source, and more. 

That's all folks! Thanks for reading. See you next week.

```markdown
```

AI

t/ai