The Hitchhiker's Guide to AI AJ Asver

Hi Hitchhikers,
I’m excited to share another interview from my podcast, this time with David Kossnick, Product Manager at Coda. Coda is a collaborative document tool combining the power of a document, spreadsheet, app, and database.
Before diving into the interview, I have an update on Parcha, the AI startup I recently co-founded. We’re building AI Agents that supercharge fintech compliance and operations teams. Our agents can carry out manual workflows by using the same policies, procedures, and tools that humans use. We’re applying AI in real-world use-cases with real customers and we’re hiring an applied AI engineer and a founding designer to join our team. If you are interested in learning more, please email founders@parcha.ai.
Also don’t forget to subscribe to The Hitchhiker’s Guide to AI:
Now, onto the interview...
Interview: Supercharging your team with Coda AI | David Kossnick
I use Coda daily to organize my work, so I was thrilled to chat with David Kossnick, the PM leading Coda’s AI efforts. We discussed how Coda built AI capabilities into their product, and their vision for the future of AI in workspaces, and he gave me some practical tips on how to use AI to speed up my founder-led sales process.
Here are the highlights:
* The story behind Coda’s AI features: Coda started by allowing developers to build “packs” to integrate with their product. A developer created an OpenAI pack that became very popular, showing Coda the potential for AI. At a hackathon, Coda explored many AI ideas and invested in native AI capabilities. They started with GPT-3, building specific AI features, then gained more flexibility with ChatGPT.
* Focusing on input and flexibility: Coda designed flexible AI to work in many contexts. They focused on providing good “input” to guide users. The AI understands a workspace’s data and connections. Coda wants AI to feel like another teammate—able to answer questions but needing to be taught.
* Saving time and enabling impact: Coda sees AI enabling teams to spend less time on busywork and more time on impact. David demonstrated how Coda’s AI can summarize transcripts, categorize feedback, draft PRDs, take meeting notes, and personalize outreach.
* Tips for developing AI products: Start with an open-ended prompt to see how people use it, then build specific features for valuable use cases. Expect models and capabilities to change. Focus on providing good "input" to guide users. Launching AI requires figuring out model strengths, setting proper expectations, and crafting the right UX.
* How AI can improve team collaboration: David shared a practical example of how AI can help product teams share insights, summarize meetings and even kick-start spec writing.
* Using AI for founder-led sales: David also helped me set up an AI-powered Coda template for managing my startup's sales process. The AI can help qualify leads and draft personalized outreach emails.
* The future of AI in workspaces: David is excited about AI enabling smarter workspaces and reducing busywork. He sees AI agents as capable teammates that understand companies and workflows. Imagine asking a workspace about a project's status or what you missed on vacation and getting a perfect summary.
* From alpha to beta: Coda’s AI just launched in beta with more templates and resources. You can try it for free here: http://coda.io/ai
David’s insights on developing and launching AI products were really valuable. Coda built an innovative product, and I'm excited to see how their AI capabilities progress.
Thanks for reading The Hitchhiker's Guide to AI! Subscribe for free to receive new posts and support my work.

Episode Links
Coda’s new AI features are available in Beta starting today and you can check them out here: http://coda.io/ai.
You can also check out the founder-led sales CRM I build using Coda here: Supercharging Founder-led Sales with AI
Transcript
HGAI: Coda AI w/ David Kossnick
Intro
David Kossnick:

Interview: AGI and developing AI Agents with Josh Albrecht, CTO of Generally Intelligent
I’ve been spending a lot of time researching, experimenting and building AI agents lately at Parcha. That’s why I was really excited I got the chance to interview AI researcher Josh Albrecht, who is the CTO and co-founder of Generally Intelligent. Generally Intelligent’s work on AI Agents is really at the bleeding edge of where AI is headed.
In our conversation, we talk about how Josh defines AGI, how close we are to achieving it, what exactly an AI researcher does, and his company’s work on AI agents. We also hear about Josh’s investment thesis for Outset Capital, the AI venture capital fund he started with his co-founder Kanjun Qui.
Overall it was a really great interview and we covered a lot of ground in a short period of time. If you’re as excited about the potential of AI agents as I am or want to better understand where research is heading in this space, as I am this interview is definitely worth listening to in full.
Here are some of the highlights:
* Defining AGI: Josh shares his definition of AGI, which he calls Human-level AI a machine’s ability to perform tasks that require human-like understanding and problem-solving skills. It involves passing a specific set of tests that measure performance in areas like language, vision, reasoning, and decision-making.
* Generally Intelligent: General Intelligence's goal is to create more general, capable, robust, and safer AI systems. Specifically, they are focused on developing digital agents that can act on your computer, like in your web browser, desktop, and editor. These agents can autonomously complete tasks and run on top of language models like GPT. However, those language models were not created with this use case in mind, making it challenging to build fully functional digital agents.
* Emergent behavior: Josh believes that the emergent behavior we are seeing in models today can be traced back to training data. For example being back to string together chains of thought could be from transcript of gamers on Twitch.
* Memory systems: When it comes to memory systems for powerful agents, there are a few key things to consider. First of all, what do you want to store and what aspects do you want to pay attention to when you're recalling things? Josh’s view is that while it might seem like a daunting task, it turns out that this isn't actually a crazy hard problem.
* Reducing latency: One way to get around the current latency when interacting with LLMs that are following chains of thought with agentic behavior is to change user expectations. Make the agent continuously communicate updates to the user for example vs. just waiting for to provide the answer. For example, the agent could send updates during the process, saying something like "I'm working on it, I'll let you know when I have an update." This can make the user feel more reassured that the agent is working on the task, even if it's taking some time.
* Parallelizing chain of thought: Josh believes we can parallelize more of the work done by agents in chain of thought processes, asking many questions at once and then combining them to reach a final output for the user.
* AI research day-to-day: Josh shared that much of the work he does as an AI researcher is not that different from other software engineering tasks. There’s a lot of writing code, waiting to run it and then dealing with bugs. It’s still a lot faster than research in the physical sciences where you have to wait for cells to grow for example!
* Acceleration vs deceleration: Josh shared his viewpoints for both sides of the argument for accelerating vs decelerating AI. He also believes there are fundamental limits to how fast AI can be developed today and this could change a lot in 10 years as processing speeds continue to improve.
* AI regulation: We discussed how it’s challenging to regulate AI due to the open-source ecosystem.
* Universal

Hi Hitchhikers!
I’m excited to share this latest podcast episode, where I interview Charlie Newark-French, CEO of Hyperscience, which provides AI-powered automation solutions for enterprise customers. This is a must-listen if you are either a founder considering starting an AI startup for Enterprise or an Enterprise leader thinking about investing in AI.
Charlie has a background in economics, management, and investing. Prior to Hyperscience, he was a late-stage venture investor and management consultant, so he also has some really interesting views on how AI will impact industry, employment, and society in the future.
In this podcast, Charlie and I talk about how Hyperscience uses machine learning to automate document collection and data extraction in legacy industries like banking and insurance. We discuss how the latest large-scale language models like GTP-4 can be leveraged in enterprise and he shares his thoughts on the future of work where every employee is augmented by AI. We also touch on how AI startups should approach solving problems in the enterprise space and how enterprise buyers think about investing in AI and measuring ROI.
Finally, I get Charlie’s perspective on Artificial General Intelligence or AGI, how it might change our future, and the responsibility of governments to prepare us for this future.
I hope you enjoy the episode!
Please don’t forget to subscribe @ http://hitchhikersguidetoai.com

Thanks for reading The Hitchhiker's Guide to AI! Subscribe for free to receive new posts and support my work.

Episode Notes
Links:
* Charlie on Linkedin: https://www.linkedin.com/in/charlienewarkfrench/
* Hyperscience: http://hyperscience.com
* New York Times article on automation: https://www.nytimes.com/2022/10/07/opinion/machines-ai-employment.html?smid=nytcore-ios-share
Episode Contents:
00:00 Intro
01:56 Hyperscience
04:52 GPT-4
09:41 Legacy businesses
11:13 Augmenting employees with AI
15:48 Tips for founders thinking about AI for enterprise
20:34 Tips enterprise execs considering AI
23:49 Artificial General Intelligence
29:41 AI Agents Everywhere
32:12 The future of society with AI
37:44 Closing remarks
Transcript:
HGAI: Charlie Newark French
Intro
AJ Asver: Hey everyone, and welcome to the Hitchhiker Guide to ai. I am so happy for you to join me for this episode. The Hitchhiker Guide to AI is a podcast where I explore the world of artificial intelligence and help you understand how it's gonna change the way we live, work, and play. Now for today's episode, I'm really excited to be joined by a friend of mine, Charlie Newark, French.
AJ Asver: Charlie is the CEO of hyper science, a company that is working to bring AI into the enterprise. Now, Charlie's gonna talk a lot about what hyper science is and what they do, but what I'm really excited to hear Charlie's opinions on is how he sees automation impacting our future.
AJ Asver: Both economically, but as a society, as you've seen with recent launch of G P T four and all the progress that's happening in AI, there's a lot of questions around what this means for everyday knowledge workers and what it means for jobs in the future. And Charlie, has some really interesting ideas about this, and he's been sharing a lot of them on his LinkedIn and I've been really excited to finally get him on the show so we can talk. Charlie also has a background in economics and management. He studied an MBA at Harvard and previously was at McKinsey, and so he has a ton of experience thinking about industry as a whole, enterprise and economics and how these kind of technology waves can impact us as a society.
AJ Asver: If you are excited to hear about how AI is gonna impact our economy, our society, and how automation is gonna change the way we work, then you are gonna love this episode of The hitchhiker Guide to ai.
AJ Asver: Hey Charlie, so great to have you on the podcast. Thank you so much for joining me.
Charlie: Aj, thank you for having me. I'm excited to

Note: This episode is best experienced as a video: https://www.youtube.com/watch?v=KDD4c5__qxc
Hey Hitchhikers!
MidJourney V5 was just released yesterday so it felt like the perfect opportunity to do a deep dive on prompting with a fellow AI newsletter . Linus creates amazing MidJourney creations every day ranging from retro rally cars to interior design photography that looks like it came straight out of a magazine. You wouldn’t believe that some of Linus’s images are made with AI when you see them.
But what I love most about Linus is his focus on educating and sharing his prompting techniques with his followers. In fact, if you follow Linus on Twitter you will see that every image he creates includes the prompt in the “Alt” text description!
In this episode, we cover how Linus shares how he went from designer to AI influencer, what generative AI means for the design industry, and we go through a few examples of prompting in MidJourney live. One thing we cover that is beneficial for anyone using MidJourney for creating character-driven stories is how to create consistent characters in every image.
Using the tips I learned from Linus, I was able to create some pretty cool Midjourney images of my own, including this series where I took 90s movies and turned them into Lego!
I also want to thank Linus for recommending my newsletter on his substack, which has helped me grow my subscribers to over a thousand now! Linus has an awesome AI newsletter that you can subscribe to here:
I hope you enjoy the episode and don’t forget to subscribe to this newsletter at http://HitchhikersGuideToAI.com.
Show Notes
Links:
- Watch on Youtube: https://bit.ly/3mWrE5e
- The Hitchhikers Guide to AI newsletter: http://hitchhikersguidetoai.com
- Linus's twitter: http://twitter.com/linusekenstam
- Linus's newsletter: http://linusekenstam.substack.com
- Bedtime stories: http://bedtimestory.ai
- MidJourney: http://midjourney.com
Episode Contents:
00:00 Intro
02:39 Linus's journey into AI
05:09 Generative AI and Designers
08:49 Prompting and the future of knowledge work
15:06 Midjourney prompting
16:20 Consistent Characters
28:36 Imagination to image generation
30:30 Bonzi Trees
31:32 Star Wars Lego Spaceships
37:57 Creating a scene in Lego
43:03 What Linus is most excited about in AI 46:10 Linus's Newsletter
Transcript
Intro
aj_asver: Hey everyone. And welcome to the Hitchhiker's guide to AI. I am so excited for you to join me on this episode, where we are going to do a deep dive on mid journey.
aj_asver: MidJourney V5, just launched. So it felt like the perfect time for me to jump in with my guests, Linus Ekenstam. And learn how to be a prompting pro.
aj_asver: Linus is a designer turned AI influencer. Not only does he have an AI newsletter called inside my mind, but he's also created a really cool website where you can generate bedtimestories for your kids. Complete with illustrations. And he is a mid journey prompting pro. I am constantly amazed by the photos and images that Linus has created using mid journey. It totally blows my mind.
aj_asver: From rally cars with retro vibes to bonsai trees that have candy growing on them. And most recently hyper-realistic photographs of interior design that looked like they came straight out of a magazine. Linus is someone I cannot wait to learn from. And he's also going to share his perspective on what all this generative AI means for the design industry, which he has been a part of for over a decade. By the way it's worth noting that a lot of the stuff we cover in this episode is very visual. So if you're listening to this. As an audio only podcast. You may want to click on the YouTube link in the show notes and jump straight to the video when you have time.
aj_asver: So if you're excited about I'm one to learn how you can take the ideas in your head and turn them into awesome images. Then join me for this episode of the Hitchhiker's guide to AI.
aj_asver: Thank you so much for joining me on the Hit

Hi Hitchhikers!
AI chatbots have been hyped as the next evolution in search, but at the same time, we know that they make mistakes. And what's even more surprising is that these chatbots are starting to take on their own personalities.
All of this got me wondering how these chatbots work? What exactly are they capable of, and what are their limitations?
In the latest episode of my new podcast, we dive into all of those questions with my guest, Kevin Fisher. Kevin is the founder of Mathex, a startup that is building chatbot products powered by large-scale language models like OpenAI’s GPT. Kevin’s mission is to create AI chatbots that have their own personalities and one day their own AI souls.
In this interview, Kevin shares what he's learned from working with large language models like GPT. We talk about exactly how large-scale language models works, what it means to have an AI soul, why chatbots hallucinate and make mistakes, and whether AI chatbots should have free will.
Let me know if you have any feedback on this episode and don’t forget to subscribe to the newsletter if you enjoy learning about AI: www.hitchhikersguidetoai.com
Show Notes
Links from episode
* Kevin’s Twitter: twitter.com/kevinafischer
* Try out the Soulstice App: soulstice.studio
* Bing hallucinations subreddit: reddit.com/r/bing
Transcript
Intro
Kevin: We built um, a, a clone of myself and um, the three of us were having a conversation. And at some point my clone got very confused and was like, who? Wait, who am I? If this is Kevin Fisher and I'm Kevin Fisher, who, which one of us is.
Kevin: And I was like, well, that's weird because we de like, we definitely didn't like optimize for that . And then we kept continuing the conversation and eventually my digital clone was like, I don't wanna be a part of this conversation with all of us. Like one of us has to be terminated.
aj_asver: Hey everyone, and welcome to the Hitchhikers Guide to ai. I'm your tour guide AJ Asper, and I'm so excited for you to join me as I explore the world of artificial intelligence to understand how it's gonna change the way we live, work, and.
aj_asver: Now AI chatbots have been hyped as the next evolution in search, but at the same time, we know that they made mistakes. And what's even more surprising is that these chatbots are starting to take on their own personalities.
aj_asver: All of this got me wondering how do these large language models. What exactly are they capable of and what are their limitations?
aj_asver: In this week's episode, we're going to dive into all of those questions with my guest, Kevin Fisher. Kevin is the founder of Mathex, a startup that is building chatbot products powered by large scale language models like OpenAI's. Their mission is to create AI chatbots that have their own personalities and one day their own AI souls
aj_asver: in this interview, Kevin's gonna share what he's learned from working with large language models like G P T. We're gonna talk about exactly how these language models work, what it means to have an AI soul, why they hallucinate and make mistakes, and what the future looks like in a world where AI chatbots can leave us on red.
aj_asver: So join me on this. As we explore the world of large scale language models in this episode of the Hitchhiker's Guide to ai.
aj_asver: hey Kevin, how's it going? Thank you so much for joining me on the Hitchhiker Guide to
aj_asver: ai.
Kevin: Oh, thanks for having me, aj. Great to be.
How large-scale language models work
aj_asver: appreciate you um, being down to chat with me on one of the first few episodes that I'm recording. I'm really excited to learn a ton from you about how large language models work and also what it means for AI is to have a soul. And so we're gonna dig into all of those things, but maybe we can start from the top for folks that don't have a deep understanding of ai.
aj_asver: What exactly is a large language model and how does it work?
Kevin: Well, so, uh, the

Hey Readers,
In today’s post, I want to share one of the first episodes of a new podcast I’m working on. In the podcast I will be exploring the world of AI to understand how it’s going to change the way we live, work and play, by interviewing creators, builders and researchers. In this episode, I interview Ammaar Reshi, a designer who recently wrote, illustrated and published a children’s book using AI! I highlighted Ammaar’s in my first post a few weeks ago as a great example of how AI is making creativity more accessible to everyone.
In the interview, Ammaar shares what inspired him to use AI to write a children’s book, the backlash he received from the online artist community and his perspective on how AI will impact art in the future. If you’re new to AI and haven’t yet tried using Generative AI tools like ChatGPT or MidJourney, this is a great video to watch because Ammaar also shows us step-by-step how he created his children’s book. This is a must-watch for parents, educators or budding authors who might want to make their own children’s book too!
To get the most out of this episode, I recommend you watch the video so you can see how all the AI tools we cover work > Youtube Video
I hope you enjoy this episode. I’ll be officially launching the podcast in a few weeks, so it will be available on your favorite podcast player soon. In the meantime, I’ll be sharing more episodes here as I record them and I would love your feedback in the comments!

Show Notes
Links from the episode
* Ammaar’s Twitter post on how he created a children’s book in a weekend:
* Ammaar’s book “Alice and Sparkles”: https://www.amazon.sg/Alice-Sparkle-exciting-childrens-technology/dp/B0BNV5KMD8
* Ammaar’s Batman video:
* ChatGPT for story writing: http://chat.openai.com
* MidJourney for illustrations: Midjourney.com
* Discord for using MidJourney: https://discord.com
* PixelMator for upscaling your illustrations: https://www.pixelmator.com/pro/
* Apple Pages for laying out your book: https://www.apple.com/pages/
* Amazon Kindle Direct Publishing for publishing your book: https://kdp.amazon.com/en_US/
Episode Contents:
* (00:00) Introduction
* (01:55) Ammaar’s story
* (05:25) Backlash from artists
* (12:20) From AI books to AI videos
* (16:20) The steps to creating a book with AI
* (18:55) Using ChatGPT to write an children’s story
* (23:45) Describing illustrations with ChatGPT
* (26:00) Illustrating with MidJourney
* (35:30) Improving prompts in Midjourney
* (37:20) Midjourney Pricing
* (40:00) Downloading image from MidJourney
* (44:20) Upscaling with Pixelmator
* (49:25) Laying out book with Apple Pages
* (53:40) Publishing on Amazon KDP
* (55:35) Ammaar shows us his hardcover book
* (56:25) Wrap-up

Full Transcript
[00:00:00]
Introduction
ammaar: I think it has to start with your idea of a story, right? I think, you know, people might think, okay, you press a button, it spits out a book, but I think it has to start with your imagination. And then we will provide that to ChatGPT to kind of give us a base for our story. I think then we'll iterate with ChatGPT almost like a brainstorming partner. We're gonna go back and forth. We're gonna expand on characters and the arcs that we might want to, you know, go through. And I think once we have that, Then we go back to imagining again.
We have to think through how do you take that script and that story and you bring it to life, how do you visualize it? And that's where MidJourney comes in. And we're gonna generate art that fits that narrative and expresses that narrative in a really nice way. And then we can combine it all together with you know, pages to create that book format.
aj_asver: Hey everyone, and welcome to the Hitchhikers Guide to ai. I'm your tour guide AJ Aser, and in this podcast I explore the world of artificial intelligence to learn how AI will impact the way we live, work, [00:01:00] and play. Now, if you're a parent like me in the middle of