chrisloy

That's just a feed for my extremely occasional newsletter, this is the blog one: https://chrisloy.dev/rss.xml

Seems I broke this with a recent change! Reinstated: https://chrisloy.dev/rss.xml

Thank you, glad you enjoyed it!

Correct!

Author here - I agree and have written about this before, though focusing a bit more on how far down the stack they might go, rather than up: https://chrisloy.dev/post/2025/03/23/will-ai-replace-softwar...

Article being discussed in this thread isn't intended to be a luddite rejection of AI. It's just a mistake I see people keep making (and have made myself) and some thoughts on how to avoid it with the tools we have today.

DataSine | Software Engineer | London | ONSITE | Full Time

DataSine (Techstars 2016) is a VC-backed tech startup bringing together machine learning and psychology to enable companies to personalise how they talk to their customers at scale. We are a small team that is growing fast, and are looking for creative engineers across the full stack to join our team in London.

You will be building a fast-growing product suite of intelligent content authoring tools, analytics and visualisation software, cloud-hosted, scalable and using a bleeding-edge machine learning stack. You will working closely with the rest of the technology team, our data science and psychology R&D team, and the rest of the company up to and including the CEO.

We are a diverse and friendly team and welcome applications from all backgrounds. This is an exciting opportunity to join a successful startup as it reaches an inflection point - if this sounds appealing then please get in touch.

Tech: Python, Flask, JavaScript, React/Redux, Postgres, AWS, Kubernetes

More details at https://datasine.com/careers/ or email me directly at chris at datasine dot com (no recruiters please)

DataSine | Software Engineer | London | ONSITE | Full Time

DataSine (Techstars 2016) is a VC-backed tech startup bringing together machine learning and psychology to enable companies to personalise how they talk to their customers at scale. We are a small team that is growing fast, and are looking for creative engineers across the full stack to join our team in London.

You will be building a fast-growing product suite of intelligent content authoring tools, analytics and visualisation software, cloud-hosted, scalable and using a bleeding-edge machine learning stack. You will working closely with the rest of the technology team, our data science and psychology R&D team, and the rest of the company up to and including the CEO.

We are a diverse and friendly team and particularly welcome applications from groups often underrepresented in the tech industry. This is an exciting opportunity to join a successful startup as it reaches an inflection point - if this sounds appealing then please get in touch.

Technologies: Python, Flask, JavaScript, React/Redux, Postgres, AWS, Kubernetes

More details at https://datasine.com/careers/ or email me directly at chris at datasine dot com (no recruiters please :)

DataSine | Software Engineer | London | ONSITE | Full Time

DataSine (Techstars 2016) is a VC-backed tech startup bringing together machine learning and psychology to enable companies to personalise how they talk to their customers at scale. We are a small team that is growing fast, and are looking for creative engineers across the full stack to join our team in London.

You will be building a fast-growing product suite of intelligent content authoring tools, analytics and visualisation software, cloud-hosted, scalable and using a bleeding-edge machine learning stack. You will working closely with the rest of the technology team, our data science and psychology R&D team, and the rest of the company up to and including the CEO.

We are a diverse and friendly team and particularly welcome applications from groups often underrepresented in the tech industry. This is an exciting opportunity to join a successful startup as it reaches an inflection point - if this sounds appealing then please get in touch.

Technologies: Python, Flask, JavaScript, React/Redux, Postgres, AWS, Kubernetes

More details at https://datasine.com/careers/ or email me directly at chris at datasine dot com (no recruiters please :)

That was my intended point (I'm a CTO at a startup).

I consider myself a decent rocket scientist (CTO) but would make a terrible brain surgeon (CEO) ;-)

Author here - completely agree with you. I think the presence of these things can be a nice shortcut to establishing expertise, but lack of them does not imply lack of expertise. These are just suggestions of things to look for, and it certainly isn't my intention that people be ruled out because of an empty GitHub profile. I hope that isn't what it sounds like.

Wow, I thought this one had just disappeared into the void. Glad to see I got people's attention and provoked debate, even if it seems much of it disagrees with my underlying assumptions.

For what it's worth, one thing I don't cover in the article is whether this is a good idea in the first place. It obviously isn't ideal, and as I say it is a difficult situation to be in. From my perspective, I think a non-technical founder is obviously better looking for a trusted former colleague or friend to join as CTO. Proven mutual history is the best thing you can lean on here.

But if this isn't possible, I would definitely espouse an interview process - however formal you prefer that to be. My post is therefore really intended as a how-to guide for someone in this awkward spot, looking to identify the skills and experience for technical leadership without the background to do so.

Of course other soft skills and fit are just as important. But any decent founder will need to identify those in all people joining the founding team. This is just intended as a step-by-step guide for those specific skills needed for a technical co-founder or CTO. I hope it is helpful for someone!

Front-End Engineer | DataSine | datasine.com | London, UK | Full time | ONSITE

DataSine (TechStars London '16, VC-backed) brings together machine learning and psychology to change how businesses interact with their customers. Our technology combines deep customer analytics with automated content customisation to improve customer experience, through the prism of personality and psychometrics.

We're looking for a motivated engineer to join our front-end team that is building our green-field web-based platform for content personalisation and customer analytics.

=> Skills needed: JavaScript, HTML5, CSS3, React, graphic design => What we look for: passionate about quality software, scientific in approach, collaborative in a small team, self-starter

If you're interested, send us your CV, your portfolio, your GitHub profile, or anything else that might impress to careers@datasine.com

Ah, I had missed that. I guess this will mitigate the risk a lot, although I would still like to have seen results against a test set of images from a different context (social media for example).

If you read the paper, the photos and labels were sourced from a dating website. In my opinion, there is a good chance that the model may be overfitting to how people wish to present themselves in that context. - e.g. framing of the photo, facial expression etc. Things with a heavy amount of cultural conditioning.

Some of the press around this seems a bit alarmist - I doubt you would see anywhere near this accuracy out in the real world.

Software Engineer | DataSine | datasine.com | London, UK | Full time | ONSITE

DataSine (TechStars 2016) brings together machine learning, psychology and finance to change how high street banks interact with their customers.

We have a small and highly skilled technical team, building our cutting edge data science platform. As demand for our products grow, we are looking for a gifted and highly motivated software engineer to join our team.

=> Skills needed: Experience in Python plus one other language a must, experience with Docker and/or real-time systems a plus, an interest in machine learning very preferable.

=> What we look for: passionate about quality software, scientific in approach, collaborative in a small team, keen to take on new responsibilities and learn new skills.

If you're interested, send us your CV, a link to your GitHub profile, or anything else that might impress to careers@datasine.com

I think what you're trying to do at Grasswire is interesting, but I feel like here you're arguing against the very point that Swartz was making. I think of all people he is the last one that you could accuse of not caring, and yet he claims not to have read the news for his entire adult life. The difference is that he voraciously read books [1] and took what I guess was a long view of issues of interest, which I think is what he is promoting here.

That said I still like the idea of being able to "touch base" with the news as it were - something that I can scan quickly and, if I'm disciplined enough, filter down to the bits that are actually relevant to me. Most days, hopefully, that's nothing at all.

[1] e.g. http://www.aaronsw.com/weblog/books2010

Thanks Martin, unfortunately it's past the point where I can edit my original comment or I'd go back and fix it. As I recall you said it off-mike in Bill Venners' talk, otherwise I would have fact-checked it!

Anyway, details aside I thought it was a great illustration of the impact on compiler performance that code complexity can have.

I've written a lot of Scala and I don't think I've seen anything as slow as even 1 line/second. Odersky was talking about Shapeless, which is an edge case in that it is a home for all the type-heavy calculations that scalac can compile but isn't currently optimised towards.

edit: reworded for clarity

Compilation speeds are largely dependent on the type of code you're writing. At London's Scala Exchange last year, Odersky claimed he can get 10k lines/second (10x slower than javac, but still not bad) out of scalac by writing simple imperative code, but libraries that make heavy use of the type system and implicit scope can be as slow as 1 line/minute.

I consider it fair to expect the compiler to be slower when it's doing more heavy lifting, but it would definitely be nice to see the exponential blow-up on the tail curbed in future.

edit: To clarify, the numbers above were what Odersky claimed; I haven't verified them. The 1 line/minute was claimed of trying to build Shapeless - I've never seen anything approaching that in the wild.

This is a really helpful guide, which I can see myself coming back to a lot.

I can't express how relieved I am to see someone else directing Scala developers away from the Cake Pattern. I've only ever seen it result in messy, confusing code.

Dispatch is a library that I initially found irritating, because in early versions it proved extremely opinionated in it's use of futures and symbolic operators.

However, as time has gone on, I've found that my coding style in Scala has evolved to the point where I want low-level libraries to work in an asynchronous way. There are now also overloaded, human-readable methods for most (if not all) of the symbolic operators.

These days it is my HTTP client of choice.

Read this before, but always good to be reminded of a classic. I'd consider most of the essays on his front page essential reading, but I particularly enjoy the wit in his essay on writing a spelling corrector in Python [0], and the flattery of our collective egos in 'Teach Yourself Programming in Ten Years' [1].

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[0] http://norvig.com/spell-correct.html

[1] http://norvig.com/21-days.html

Hi, thanks for the feedback! Yeah I'm aware the look on Chrome on my Android is pretty terrible, which is on my list to get sorted. Looks like it's even worse on iOS! At the moment its basically using the default site template from Jekyll which is apparently very much not mobile first!

Also re: contact, there's a link to my Twitter account but not much more. Cheers for letting me know anyway!

Edit: Grokked media queries; hopefully a little nicer on mobile now.

I work for a large organisation and am not really in a position to go into details about what we'd be using it for at the moment, sorry.

Suffice it to say that we're pretty early on in the project and so far we're using Akka Cluster for sharing transient state between nodes, without the need for any kind of persistence store being used. My post was in response to the pain I had in getting over the initial hurdle of getting a prototype running.

The way we have it set up, if the JVM crashes then it will mean that the health check url used by the load balancer will become unresponsive. This would lead to the auto scaling group downing the instance and replacing it with a new one. So, the same node wouldn't ever need to rejoin.

Thanks! The SDK has some "quirks" (and isn't especially Scala-friendly) but I reckon it's ultimately pretty powerful. My main gripe is the lack of real-world examples in the docs - hence my desire to write this up.