Jim Nielsen:

I heard Paul Ford, a professional writer for Wired and other publications, say something on The Aboard Podcast, Episode 3 that resonated. Referring to a moment when lots of folks online were looking for the perfect writing environment, he said the software tool, workflow, environment, whatever, it didn’t matter. You could do it with pen and paper if you want.

The only technology that you need is deadlines.

Ooff. Yup, that resonates. That’s been the best piece of technology for making me productive too.

See also arbitrary deadlines are actually awesome by yours truly.

To say that Jeff Adams is a trailblazer when it comes to speech technology is an understatement. Along with many other notable accomplishments, his team at Amazon developed the Echo, Dash, and Fire TV changing our perception of how we could interact with devices in our home. Jeff now leads Cobalt Speech and Language, and he was kind enough to join us for a discussion about human computer interaction, multimodal AI tasks, the history of language modeling, and AI for social good.

Awesome piece by Wenbin Fang, creator of Listen Notes:

Most of time, the biggest obstacle of building & shipping things is over thinking. What if this, what if that. Boy, you are not important at all. Everyone is busy in their own life. No one cares about you and the things you build, until you prove that you are worth other people’s attention. Even you screw up the initial product launch, few people will notice. Think big, start small, act fast. It’s absolutely okay to use the boring technology and start something simple (even ugly), as long as you actually solve problems.

In our last episode of the year, I talk with Maria Boland Ploessl. Maria’s path to technology has been interesting to say the least. A Saint Paul native, she studied Spanish and Latin American studies in college. In 2016, after living in a few different cities (even a year-long stint in Brazil), she moved back to Minnesota. Now, she’s the Executive Director of Minnestar, a non-profit organization with the aim of supporting and growing Minnesota’s tech community.

Maria talks to me about what Minnestar does, the work they’re doing to bring more people of underrepresented groups into tech, married life and how she’s grown from it, and parenthood.

It’s the time of year for 2017 wrap-ups and 2018 predictions. We expect these from bloggers and news sites, but this seems like an odd post coming from GitHub:

Data is on the rise, placing an even greater emphasis on security, cloud, and open source. Jason Warner, SVP of Technology, shares his predictions on the major technology trends for 2018.

I wouldn’t call too many of these predictions bold, but it’ll be interesting to see how well they hold up.

Our Slack community wanted to hear about AI-driven drug discovery, and we listened. Abraham Heifets from Atomwise joins us for a fascinating deep dive into the intersection of deep learning models and molecule binding. He describes how these methods work and how they are beginning to help create drugs for “undruggable” diseases!

This interview with Evan You (founder of Vue.js) is all about the path that led him to being able to work full time on open source. It’s a good read 👍

(Want to dive deeper? We’ve interviewed Evan on The Changelog #184 as well as Request For Commits #12)

An awesome list of tutorials on how to build it yourself; from Git to video games to neural networks and more.

It seems like all we hear about are the great use cases for GenAI, but where should you NOT be using the technology? On this episode Chris and Daniel share their hot takes and bad use cases. Some may surprise you!

IEEE Spectrum reports on the return to physical buttons and dials, Microsoft released GenAIScript, iFixit’s Elizabeth Chamberlain announces a big Right to Repair win, Daniela Baron reimagines technical interviews & John O’Nolan, shares some thoughts on open source governance and how to create trust within technology, communities, and media

Elham Tabassi, the Chief AI Advisor at the U.S. National Institute of Standards & Technology (NIST), joins Chris for an enlightening discussion about the path towards trustworthy AI. Together they explore NIST’s ‘AI Risk Management Framework’ (AI RMF) within the context of the White House’s ‘Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence’.

Robert Ross joins us in CrowdStrike’s wake to dissect the largest outage in the history of information technology… and what it means for the future of the (software) world.

Benn Stancil’s weekly Substack on data and technology provides a fascinating perspective on the modern data stack & the industry building it. On this episode, Benn joins Jerod to dissect a few of his essays, discuss opportunities he sees during this slowdown & explain why he thinks maybe we should disband the analytics team.

Valerie Phoenix from Tech By Choice joins Amal & Kball to tell them all about her non-profit that’s passionate about helping people interested in technology, no matter their experience level.

The Astro team releases a new documentation builder, legendary computer scientist Donald Knuth plays with ChatGPT, over 500 volunteer mods have signed an open letter to Stack Overflow Inc, Reddit faces a revolt due to their new API pricing & the Technology Innovation Institute release Falcon, a new open source LLM that’s topping Hugging Face’s leaderboard.

I’ve found myself referencing this a couple times recently. Both of those conversations were in the context of React, but the principle applies to every over-adopted technology.

There are a ton of problems around building LLM apps in production and the last mile of that problem. Travis Fischer, builder of open AI projects like @ChatGPTBot, joins us to talk through these problems (and how to overcome them). He helps us understand the hierarchy of complexity from simple prompting to augmentation, agents, and fine-tuning. Along the way we discuss the frontend developer community that is rapidly adopting AI technology via Typescript (not Python).

What can art historians and computer scientists learn from one another? Actually, a lot! Amanda Wasielewski joins us to talk about how she discovered that computer scientists working on computer vision were actually acting like rogue art historians and how art historians have found machine learning to be a valuable tool for research, fraud detection, and cataloguing. We also discuss the rise of generative AI and how we this technology might cause us to ask new questions like: “What makes a photograph a photograph?”

Emilio Cobos Álvarez:

So this week I landed a few patches that completely removed XUL layout from the Firefox codebase.

This means that (modulo a few exceptions documented below) all the Firefox UI is using regular web technology to render (mostly CSS flexbox).

This was a rather big effort (first filed 9 years ago in bug 1033225), and I wanted to document some of the things that I learned during the process, some of the things and decisions that made it possible, and some of the things that I would’ve maybe done differently.

Fabien Sanglard’s advice to writers:

By starting with a markup based generator, you rob yourselves of the opportunity to learn the core technology running the whole internet. Instead you will learn to use a layer built on top of it with little to no re-usable knowledge. Inevitably, you will want to do something the framework did not forecast and will come up with convoluted ways to do it anyway.

Additionally, depending on how long you will write and how often you do it, setting up a generator may be counterproductive.

The best way to avoid the YAGNI trap is to start as simple as possible and only add tech when you’re absolutely sure the ROI is higher than the TCO.

Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. But using these LLMs in isolation is often not enough to create a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.

This library is aimed at assisting in the development of those types of applications.

LangChain is designed to help with prompts, chains (sequences of calls), data augmented generation, agents, memory & evaluation tasks.

If you are the technical co-founder or early engineering lead at a startup, and you want to talk about your microservices, hand-rolled CI/CD, in-house monitoring stack, or any other unique part of your stack, I will say: Cool. Let’s riff. Take me deep, I’m ready.

But there’s something I’m likely to tell you in return, something I’ll probably insist you’re overlooking and need to internalize as soon as possible: Your technology stack is not the product.

It’s easy to get too focused on our tech and tools. Sometimes we think it’s our competitive advantage, other times it’s merely personal fascination and intellectual stimulation. This post is a good reminder:

A mindset of technology being the means, not the end, is uncomfortable. But it will help you stay focused on what matters most (the product and your customers), avoid wasteful misadventures, and maximize the company’s chance of success.

While at EMNLP 2022, Daniel got a chance to sit down with an amazing group of researchers creating NLP technology that actually works for their local language communities. Just Zwennicker (Universiteit van Amsterdam) discusses his work on a machine translation system for Sranan Tongo, a creole language that is spoken in Suriname. Andiswa Bukula (SADiLaR), Rooweither Mabuya (SADiLaR), and Bonaventure Dossou (Lanfrica, Mila) discuss their work with Masakhane to strengthen and spur NLP research in African languages, for Africans, by Africans.

The group emphasized the need for more linguistically diverse NLP systems that work in scenarios of data scarcity, non-Latin scripts, rich morphology, etc. You don’t want to miss this one!

This week we’re joined by Christina Warren, Senior Developer Advocate at GitHub, and a true tech and pop culture connoisseur. From her days at Mashable covering the intersections of entertainment and technology, to Gizmodo, to Microsoft, and now her current role at GitHub we talk with Christina about her journey from journalist to developer, and the latest happenings coming out of GitHub Universe.

BTW, we’re planning to get Christina on Backstage in the new year to talk about Plex, MakeMKV, and all things that go into hosting your own media server. Drop a commment on this episode with a +1 if you want to see that happen.

Online platforms and their users are susceptible to a barrage of threats – from disinformation to extremism to terror. Daniel and Chris chat with Matar Haller, VP of Data at ActiveFence, a leader in identifying online harm – is using a combination of AI technology and leading subject matter experts to provide Trust & Safety teams with precise, real-time data, in-depth intelligence, and automated tools to protect users and ensure safe online experiences.

Let’s see if this ages well, but I do agree that Armin shares some good points. Scale comes with mainstream, and mainstream Mastodon isn’t quite possible if we can’t all agree (and we can’t). This is probably Mastodon’s most obvious achilles heel — agreement in a decentralized, federated, you can “do what you want” world.

…before we even hit the issue of the technology, we hit the issue of there being absolutely no agreement of what the thing should look like or what the issue actually is and that’s I think much more interesting.

Some people claim the solution to the technical scalability issue is huge instances, some other people have the belief that the actual intended design and solution were micro-instances of in extreme cases a user each.

Then there’s moderation, which if you’ve ever run any community forums, Slacks, etc you’ll know that you’re going to have to moderate and deal with people problems.

On the topic of moderation the very same issue is even more absurd. Some instances want uncontrolled free speech where some instances effectively are pure shit-posting instances which are completely de-federated from the most of the fediverse as a result. Other instances really like to control their content, where some popular ones such as fosstodon ban all languages than English as a result to allow moderation. There also is no real agreement on if larger or smaller instance are going to make the problem of moderation better or worse.

This panel discussion was recorded at a recent event hosted by a company, Aryballe, that we previously featured on the podcast (#120). We got a chance to discuss the AI-driven technology transforming the order/fragrance industries, and we went down the rabbit hole discussing how this technology is being adopted at large, well-established companies.

Tomasz Cichociński:

It’s completely fine to deepen one technology or one programming language knowledge. It’s totally fine to just have a 9-5 job that pays the bills. But if you aim to level up, I’m highly recommending trying something new to broaden your perspective.

If you’re writing JavaScript, try some functional language like Elixir or ReScript. If you’re already familiar with high level, garbage collected languages, try something low level like Rust or even C/C++. Play with different concurrency model like coroutines or actor model.

You don’t need to change your job, you don’t need to ship products with it. Try to create something fun. You’ll thank yourself later.

Dan Hollick nerds out on the history and anatomy of a QR code. This bit at the end is super cool:

Perhaps the coolest thing about QR codes is that Denso Wave, the company that invented them, never exercised their patent and released the technology for free!

The new stable diffusion model is everywhere! Of course you can use this model to quickly and easily create amazing, dream-like images to post on twitter, reddit, discord, etc., but this technology is also poised to be used in very pragmatic ways across industry. In this episode, Chris and Daniel take a deep dive into all things stable diffusion. They discuss the motivations for the work, the model architecture, and the differences between this model and other related releases (e.g., DALL·E 2).

(Image from stability.ai)