Nelson

[Gena Gorlin @Gena_I_Gorlin]

Whoa- this is the most fantastic pedagogical tool I’ve encountered in as long as I can remember (which is a helluva high bar, considering who I live with).

I’m so excited to have my 6yo play with it! And you all should too.

https://t.co/vv8QVMO7VP

[Haoliang Gao @popomore]

awk '!seen[$0]++' filename.txt > output.txt

这种方法去重还是第一次见到

[Marshall Wu @liujinmarshall]

我还记得在本科时候的Linux编程课上，学习了etc和usr的全称——Essential Text Configuration, Unix System Resource。特别是后者很容易望文生义以为是user。

[Nelson @zhaonc]

语音识别时怎样区分多人谈话

https://t.co/5ZohsmgfwC

[jerrywang.cn @jerrywanglaus]

最近在个人生活和工作里都遇到了让我纠结的事情，又一次祭出了这个网站，真是整理思路的好帮手啊
↳https://t.co/9XNI1EQ94r
感谢当时分享这个资源的推友！

[Sam Hogan 🇺🇸 @samhogan]

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 >$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 @langchain 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 @dannypostma'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://t.co/VA35Jn2sti - an AI-first code editor that could very well change how software is written.
2. https://t.co/DdZYB0H7Hq - AI for legal practices
3. https://t.co/ppAFpUNSHo - 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.

[Nelson @zhaonc]

@bthdonohue @InstapaperHelp @instapaper Looks good now. Thanks Brian!

[Nelson @zhaonc]

Is Instapaper still alive? 🤔 The web UI has been crooked for weeks and support email is blocked.
@InstapaperHelp @instapaper

[Hacker News Bot @newsycombinator]

Pandas v2.0 Released https://t.co/QPu2YoY5YB

[Santiago @svpino]

11 ways ChatGPT saves me hours of work every day, and why you'll never outcompete those who use AI effectively.

A list for those who write code:

1 of 16

[Hui Zheng @xyz98]

母语非英语者如何快速写一篇满意的英文文章？可尝试用如下pipeline：
1. 用母语写下梗概，用 #ChatGPT 扩充
2. 用 #DeepL 译成英文
3. 将英文用 #quillbot 润色加工
4. 最后用 #grammerly 定稿

[图拉鼎 @tualatrix]

来一个视频版本的 ChatGPT 如何辅助写代码的过程。真的比 GitHub Copilot 还要好用。

[App Economy Insights @EconomyApp]

Choose your fighter
$AAPL $MSFT $GOOG $AMZN

[Yishan @yishan]

Because it is not TOPICS that are censored. It is BEHAVIOR.

(This is why people on the left and people on the right both think they are being targeted)

The problem with social networks is the SOCIAL (people) part. Not the NETWORK (company).

[Wes Gray 🇺🇸 @alphaarchitect]

Factor Investing in Sovereign Bond Markets--
by @larryswedroe

https://t.co/8TQM2Cmkko

[Project Jupyter @ProjectJupyter]

Are you a #Jupyter user ? The new release of #IPython 8.0.0 makes your experience better in notebooks. Tracebacks now highlights
errors location and contain input number for better readability, and faster debugging. Many other improvements: https://t.co/FILSJdI8GK

[virushuo @virushuo]

才知道我这个2017的文章又火了。 https://t.co/g5gGlhcXHs 今天我要补充的是，不要相信苹果。上一代送给我们的是开发自由的互联网，我们把它搞成了都是广告的比较中心化互联网，这不好但是能用。但是我们现在必须阻止苹果把它变成完全封闭的大规模监控网络。必须阻止它，不然下一代会骂死我们。

[Nelson @zhaonc]

Collaboration among data scientists, and between them and practitioners, clean and unbiased data, and continuous monitoring and fine tuning.

True in every application of data science. https://t.co/59jmj3gfWy

[Nelson @zhaonc]

55g静电容太重了，连续几周都打出了腱鞘炎。刚刚重新换上了红轴，感觉就是行云流水。