The Most Interesting Tech Reads in February 2023
1. Weaponizing hyperfocus: Becoming the first DevRel at Tailscale
- A case study of the new concept — devrel
In Spring 2022, I sat down and started to take DevRel seriously at Tailscale. I had heard about the practice in the past, but I had never done it before. I knew that a lot of it was talking about a product to people and finding new and interesting ways to use it, but a lot of that seemed too vague to really have any solid place to start from. It’s like walking into an empty room and having no idea where to start decorating.At some level though, it felt right for me in a way that I have difficulty describing. It’s the same feeling of it being right that I get from working in software in the first place. It felt like the type of rabbit-hole you could spend an entire career in
2. Launch HN: Needl (YC S22) — Simple search across all your apps
- Cool project, I’ve been looking for this for a long time — but simply not enough. I need to search across at least ZD HC and Github Repos.
To give generated answers to questions, we take the first results on Needl and run them through GPT-3 and use an engineered version of the user’s query to prompt GPT-3 accordingly. It’s similar to what Phind.com (Hello Cognition) and Perplexity.ai do with web search.
3. Ask HN: Suggestions for working effectively with junior devs?
- Thinking about owns dreams? Beware what you wish for.
The devs on my team are smart but mostly naive about getting stuff done in the real world. I’m sure they’ll figure it out over time. But in the meantime, my days are quite frustrating because I’m in teaching mode most of the time instead of building stuff.Unfortunately you will either need to change your attitude or change teams. In your position, the expectation is that you are mostly a working through others and acting as a force multiplier of their work….And in general, there are two ways to go. The first is to accept the fact that many senior engineering and most staff engineering gigs are more about this kind of mentorship approach than actually doing work. And basically accept that the prime “getting shit done” years of your career are done and you will mostly be working in this new way now.The second is to change jobs where you are back in the driver seat. As a senior engineer, these positions DO exist even at FAANG (over on my team, I am the most junior with 12 years of experience). However, the more senior you get, the harder it is to find a role like this, especially in big tech — its the rare exception. Startups and consulting gigs probably better align with wanting to be hands on keyboard, but at the price of a paycut.
4. Responsibility — by Kent Beck
- Kent Beck always presses for Skin in the Game
CEO: I take full responsibility for these layoffs.Iñigo Montoya: You keep using that phrase. I do not think it means what you think it means.
5. If it helps, this likely is coming
6. Google testing ChatGPT-like chatbot ‘Apprentice Bard’ with employees
- Originally this was ChatGPT Passes Google Coding Interview for Level 3 Engineer with $183K Salary
underestimate the amount of work needed to make improvements. We’ve been a year away from full self driving cars for the last six years, and it seems like people are getting more cautious in their timing around that instead of getting more optimistic. Robotic manufacturing- driven by AI- was supposedly going to supplant human labor and speed up manufacturing in all segments from product creation to warehousing, but Amazon warehouses are still full of people and not robots.What I’ve seen again and again from people in the field is a gross underestimation of the long tail on these problems. They see the rapid results on the easier end and think it will translate to continued process, but the reality is that every order of magnitude improvement takes the same amount of effort or more.On top of that there is a massive amount of subsidies that go into training these models. Companies are throwing millions of dollars into training individual models. The cost here seems to be going up, not down, as these improvements are made.I also think, to be honest, that machine learning researchers tend to simplify problems more than is reasonable. This conversation started with “highly scalable system from scratch, or an ultra-low latency trading system that beats the competition” and turned into “the parsing of and generation of this kind of code”- which is in many ways a much simpler problem than what op proposed. I’ve seen this in radiology, robotics, and self driving as well.Kind of a tangent, but one of the things I do love about the ML industry is the companies who recognize what I mentioned above and work around it. The companies that are going to do the best, in my extremely bias opinion, are the ones that use AI to augment experts rather than try to replace them. A lot of the coding AI companies are doing this, there are AI driving companies that focus on safety features rather than driver replacement, and a company I used to work for (Rad AI) took that philosophy to Radiology. Keeping experts in the loop means that the long tail isn’t as important and you can stop before perfection, while replacing experts altogether is going to have a much higher bar and cost.
7. One dialogue that is missing from the current GPT conversations is about where these technologies will be primarily implemented. There are two places GPTs can be used: Product-level: Implemented within products that companies buy Process-level: Implemented within processes of a company by an analyst — similar to how companies use data analysts today
- An interesting new dimension on ChatGPT Enterprise
8. Where will the impact of ChatGPT fall? (from my email) — Marginal REVOLUTION
- An interesting new dimension on ChatGPT Enterprise
One dialogue that is missing from the current GPT conversations is about where these technologies will be primarily implemented.There are two places GPTs can be used:Product-level: Implemented within products that companies buyProcess-level: Implemented within processes of a company by an analyst — similar to how companies use data analysts todayIf the future has primarily Product-level GPT, then companies like Microsoft clearly win because they have the products (like Teams) where the tech will be embedded and if you want the productivity gains from GPTs you have to go through them.If the future has more Process-level GPT, companies like Zapier and no-code platforms win, because they will be the tools that companies use to implement their custom prompts. (although maybe a “Microsoft Teams Prompt Marketplace” wins as well)
9. bash — How to access the metadata of a docker container from a script running inside the container? — Stack Overflow
- How to distinguish between various containers
You can query unix sockets with new curl — but All that said — this isn’t very secure, and environment variables are probably a better bet.
10. ossu/computer-science: Path to a free self-taught education in Computer Science!
- A big temptation
11. Complete External Attack Surface Management
- Interesting product for external attack surface detection, testing endpoints with scriptattacks
12. The Four Horsemen of the Tech Recession — Stratechery by Ben Thompson
- Great work on imaginery + AI + a comprehensible model to current wave of layoffs in Tech
13. Anti-pattern — Wikipedia
- Great metaphor to learn — a warning sign.
A Big Ball of Mud is a haphazardly structured, sprawling, sloppy, duct-tape-and-baling-wire, spaghetti-code jungle. These systems show unmistakable signs of unregulated growth, and repeated, expedient repair. Information is shared promiscuously among distant elements of the system, often to the point where nearly all the important information becomes global or duplicated.The overall structure of the system may never have been well defined.If it was, it may have eroded beyond recognition. Programmers with a shred of architectural sensibility shun these quagmires. Only those who are unconcerned about architecture, and, perhaps, are comfortable with the inertia of the day-to-day chore of patching the holes in these failing dikes, are content to work on such systems.
14. Guido van Rossum: Python and the Future of Programming
15. MotherDuck: Big Data is Dead
- DuckDB is positioning itself for doing analysis on what would previously have been considered “big data” but on your laptop. So, for data scientists who know SQL but prefer R or Python — there are limitations there as both R and Python work in memory (there are workarounds, but they are complicated).
DuckDB allows you to analyse larger datasets (10–100gb lets say) on your machine with very fast SQL queries — whereas before you would have had to load into your warehouse manually to be able to perform those types of analyses or transformations, adding more boring work for data scientists.DuckDB have also added some really nice improvements to their SQL syntax, making SQL nicer to work with for data scientists who are used to the flexibility R and Python allow them.
16. From Bing to Sydney — Stratechery by Ben Thompson
- Unsure how much this matter, and it may be a fad, but having mainstream debates about sentient AI AND unidentified flying objects within a week is borderline weird.
Look, this is going to sound crazy. But know this: I would not be talking about Bing Chat for the fourth day in a row if I didn’t really, really, think it was worth it. This sounds hyperbolic, but I feel like I had the most surprising and mind-blowing computer experience of my life today.
17. Good times create weak men @ tonsky.me
- The biggest a-ha moment of the talk was that if you are working on Y and Y is based on X, that does not imply automatically that you would know X also. Even if the people who build X are still around, knowledge does not spread automatically and, without actual necessity, it will go away with the people who originally possessed it.
18. An example of how to use command-line tools to transcribe a viral video of Cardi B
- simply a great job here
nspired by the following exchange on Twitter, in which someone captures and posts a valuable video onto Twitter, but doesn’t have the resources to easily transcribe it for the hearing-impaired, I thought it’d be fun to try out Amazon’s AWS Transcribe service to help with this problem, and to see if I could do it all from the bash command-line like a Unix dork.
19. KJF-2023. Day 1. Tyler Cowen. Interview — YouTube
20. The Ryan Holiday Reading Recommendation Email — RyanHoliday.net
- Wonderful Kids Section in abstract principles it contains
My wife and I re-read This Is Your Time by Ruby Bridges to both boys, which showcases the incredible bravery of the first black child to integrate an all-white elementary school in New Orleans in 1960. This is not the distant path…Ruby Bridges is the same age as my in-laws. We felt it was best to follow that up with another favorite, Each Kindness, and its lesson of how small acts of kindness (like Ruby’s teacher Mrs. Henry) can change the world. My oldest and I just finished the beautiful illustrated version of Harry Potter and the Sorcerer’s Stone (a popular seller last month!). We promised him a lego set from each book, so he was excited to go straight into the next one in the series, Harry Potter and the Chamber of Secrets.
21. More alternatives to Google Analytics [LWN.net]
- Learning about Countly as I’ve had one of their solution architects at home for lunch
22. command line — How to use Ffmpeg to convert wma to mp3 recursively, importing from txt file? — Ask Ubuntu
- Converting lecture recordings from wma to mp3 for the noise reduction with sox later
23. petewarden/spchcat: Speech recognition tool to convert audio to text transcripts, for Linux and Raspberry Pi.
24. Transcribe Audio — Python Tutorial
25. Transcribing mp3 to text (python) →
26. My excellent Conversation with Will MacAskill — Marginal REVOLUTION
- My neighbor is reading his ‘What we owe the future’ saying 10% of all people who ever existed live NOW.
27. Text is All You Need — by Venkatesh Rao — Ribbonfarm Studio
- Almost hegelian definition of personhood as the crucial moment seems to be the moment of Acknowledgement as person ?
The Copernican MomentSo what’s being stripped away here? And how?The what is easy. It’s personhood.By personhood I mean what it takes in an entity to get another person treat it unironically as a human, and feel treated as a human in turn. In shorthand, personhood is the capacity to see and be seen.This is obviously a circular definition, but that’s not a problem so long as we have at least one reference entity that we all agree has personhood. Almost any random human, like me or you (I’m not entirely sure about a few, but most people qualify), will do. So long as we have one “real person” in the universe, and agree to elevate any entity they treat unironically as a person as also a person, in principle, we can tag all the persons in the universe.In Martin Buber’s terminology (ht Dorian Taylor for suggesting this way of looking at it), X is a person if another person relates to it in an I-you way rather than an I-it way.
28. Presentations — Benedict Evans
29. OpenAI /ChatGPT — How to Use it With Python
- Great write-up on how simple it is to start using Open AI models via API (currently free trial, of course)
- I have a formalist approach to interpretation, i.e. that the knowledge of forms used by an artist (or a deliberate lack of thereof) lets you communicate better with the work or art, same as understanding the language improves the communication with another human being
31. Why we are teaching this class · Missing Semester
- Looks really useful. Sometimes things that catch your eye take time, and often they are not forgotten (OSSU!). But I’m bookmarking all the same. Memory is a trickster.
During a traditional Computer Science education, chances are you will take plenty of classes that teach you advanced topics within CS, everything from Operating Systems to Programming Languages to Machine Learning. But at many institutions there is one essential topic that is rarely covered and is instead left for students to pick up on their own: computing ecosystem literacy.
32. What Is Nix
The most basic, fundamental idea behind Nix is this:Everything on your computer implicitly depends on a whole bunch of other things on your computer.All software exists in a graph of dependencies.Most of the time, this graph is implicit.Nix makes this graph explicit
33. How to Design Programs > Preface
The typical course on programming teaches a “tinker until it works” approach. When it works, students exclaim “It works!” and move on. Sadly, this phrase is also the shortest lie in computing, and it has cost many people many hours of their lives. In contrast, this book focuses on habits of good programming, addressing both professional and vocational programmers.By “good programming,” we mean an approach to the creation of software that relies on systematic thought, planning, and understanding from the very beginning, at every stage, and for every step. To emphasize the point, we speak of systematic program design and systematically designed programs. Critically, the latter articulates the rationale of the desired functionality. Good programming also satisfies an aesthetic sense of accomplishment; the elegance of a good program is comparable to time-tested poems or the black-and-white photographs of a bygone era. In short, programming differs from good programming like crayon sketches in a diner from oil paintings in a museum.