Consuming AI-related content
Working in AI, one of the never ending tasks is to keep up with the state-of-the-art. Everything constantly evolves, from algorithms, architectures, datasets, benchmarks, to companies, competition or regulation, and the lists could go on for much longer. It is even worse if you’re in research as you constantly need to watch out for parallel advancements that might cost you a publication. I only got a small dosage of that dynamic during my Master’s thesis, which ultimately resulted in my decision not to pursue a PhD program for the time being, even though I love doing research.
Keeping up is so important (and fun!) that I want to use this entry to both collect and reflect on some sources of information I consume on a regular basis.
Social Media
In the end, companies are just a collection of people with an overarching common cause (individual motivations may vary). And many of these people will use social media to share experiences and discussions, some keep it more personal and casual, some more professional. I like to hear both, that is why I follow lots of people on different platforms, which I’ll briefly mention in the following.
My relationship to Twitter (now X) is difficult. It is still the best ressource for short-lived discussions about current topics and as someone working in AI and tech, it feels essentiell for keeping up with the accelerating pace of AI advancements. There are great newsletters and podcasts which curate some of these discussions and announcements, but it somehow feels much better to be there when it happens instead of reading a summary afterwards. I have this weird FOMO when it comes to AI advancements even though I would probably live the same life without being on that platform as I do not engage on much discussion, rather just consume and bookmark for future reference. Most of the changes since Musk’s takeover made the platform a generally worse place for sensible discussion, but it still has the critical mass to be relevant and many people couldn’t be bothered to switch to an alternative platform. That’s why I’m still on there, but I have muted a lot of words, blocked a lot of accounts and turned off notifications, which are all necessary means for avoiding rage baits, crypto scam bots, and political nonsense.
People I recommend following:
- @hxiao
- @chipro
- @paul_cal
- @NielsRogge
- @vikhyatk
- @huybery
- @skalskip92
- @burkov
- @giffmana
- @karpathy
- …
Making that list I realized there are way too many good people I follow that I do not want to bloat this blog entry, I might need to do a separate collection at some point. Although there are Twitter alternatives gaining popularity, I have not tried Mastodon yet and I find the current experience on Bluesky not captavative enough (even though I love the whole custom feed and starterpack concept). I really root for Bluesky though as it seems like Twitter in the old days, with both the good and bad aspects to it.
Hackernews
Hackernews is great, it’s simple, clean, and carries interesting articles. There is plenty of non-AI stuff on there, which I find refreshing and broadens my horizon. I don’t have an account registered as I don’t engage in much discussion there, but the comment section is quite interesting. It’s enough for me to skim it once or twice daily on my phone using Harmonic.
YouTube
YouTube has always been the go-to platform for visual explanations, free lectures, and code-along tutorials. This is also the place I go to when it comes to setting up and configuring my home servers as I draw a lot of inspiration from other setups. But on YouTube there are also a lot of content creators (hate that word) that strike the perfect balance between informative and entertaining content, which is rarely the case for other media. The prime example for this is Jeff Delaney with his Fireship channel - can’t recommend enough. For more in-depth knowledge, the goat Andrej Kaparthy fortunately regularly quits his job to bring us high quality content around AI and mostly LLMs recently.
To escape the attention-jailing mechanisms that come with YouTube, I use it on my phone through NewPipe, which gets rid of the shorts, recommendations, and ads. The only real feed on there is a chronologically sorted list of recent uploads from channels you actively follow. It comes with free download capabilities, too. To master the balance between exploration and exploitation, I still visit the regular YouTube site on my desktop PC in hopes of discovering the next Fireship of Kaparthy.
There are so many good discussions happening on reddit when it comes to local AI, it is amazing. I find r/LocalLLaMA to be one of the best places to look for tutorials, experiences, and debugging help. I would probably make much more use of it if I had an AMD GPU, but as I am blessed with a 16GB NVIDIA card, everything I want to try often just works (except the occasional CUDA version mismatch). If you’re into other modalities, there’s also r/StableDiffusion, which (just like locallama) transcends its original naming and discusses generative AI for images/videos in general, often with experiences of local fine-tuning, prompting techniques, and tech stack discussions. Good stuff.
Discord
There is just not enough time in a day for me to actively partake in discussions on Discord servers. I have tried with the LAION one, HuggingFace, and many others, but often found myself just muting them instantly, and then forgetting about it. I think it’s great for people who work in a specific domain and want to engage in discussions with strangers about it, but that is just not my vibe right now. I use Discord privately with friends, so I know my way around it, I even moderated the Discord server of my former employer, but that’s about it.
Newsletters
I have tried many newsletters and discovered that I personally like the ones most that are collections of ressources rather than in-depth discussions of topics (that I might not be interested in). Ressources I can just skim and go deeper into if they interest me. The best newsletter for that is TLDR - it’s clean, concise, and arrives daily - perfect for keeping up. It also handles sponsors in a consistent pattern so that my brain can automatically recognize (and ignore) them. Good stuff. I also liked the Latent Space newsletter but these days I just listen to their podcast instead.
There are many good domain-specific newsletters I tried, e.g. for NLP, but I found myself to currently be too much of a generalist for these to make sense. I will for sure add them back once I have a full-time job in the industry, looking forward to that!
Podcasts
There are plenty of activities that must be done where my brain yearns for intellectual stimulation. Cooking, doing laundry, cleaning the appartement, going grocery shopping - all of these happen mostly on autopilot for me and that is the best time to listen to podcasts. Depending on my mood I choose more information-dense or more entertaining ones. Generally, I like this medium as there are often guests from the industry that tackle really hard challenges of today’s AI applications and getting their personal perspective and expertise in this more “lowkey” podcast setting is great.
These are my favourites:
- Latent Space (very informative, great guests, timestamps)
- Hard Fork (great hosts, very entertaining, also covers politics - in a good way)
- programmier.bar (German, “only” bi-weekly so not as up2date)
Blogs
Unfortunately I don’t read blogs as often these days, mainly because they are not as convenient as the other medias mentioned. I am not an RSS-feed kinda guy, so I either check these blogs manually from time to time or just stumble upon some posts that made it into one of the other formats. Most personal blogs are great for relatively unfiltered opinions about the individually perceived progress of AI without the visionary hype buzzwords around it used by shareholders. Company blogs on the other hand are great for in-depth tech articles about problems they have faced. The price of reading one or two paragraphs about their product is worth it for me as long as they don’t tweak the numbers in their favor, which sometimes is hard to verify.
Personal blogs I like reading:
- Simon Willison (puts latest AI trends and libraries to test, practical, hands-on)
- Chip Huyen (technical, well-written)
Interesting company blogs:
- Qdrant (open-source vector database, case studies, concepts)
- Jina AI (NLP fine-tunings, LLM tooling)
- Roboflow (Computer Vision, fine-tuning cookbooks, real-world use cases)
- HuggingFace (everything open-source, high frequency and variety)
Thanks for sticking to the end, see you in the next entry!