tomwphillips

This is pretty niche cheddar. I doubt it’s being sold at a corner shop alongside warm cans of Carlsberg!

I imagine it will be smuggled out of the country immediately.

No cheese buyer at a large retailer in the UK would touch it. It’s not worth the risk.

Similarly at the artisan end of the market, it’s a small world (like the article says) - why would you risk your personal and commercial reputation buying stolen goods? You wouldn’t.

>So much software also just takes for granted that it should be allowed on the Internet.

Indeed, and I find it a problem on land too. I use Miro at work and it is _awful_ on an unreliable internet connection, like on trains in the UK.

I really value local-first software for this reason. I’d like to see more of it.

That didn’t occur to me.

As an IPython/Jupyter user for over a decade and Mac user for two decades, it’s not something I personally care about or an issue I’ve felt.

Why “modern”? I suggest you use more descriptive adjectives.

All those things are true, but there’s risk associated with going with such a new project with no current monetisation path (especially for something that my colleagues might be putting information into that we might need to access/modify for many years or even indefinitely).

Open source isn’t a business model.

There’s a lot more to running an application than that.

Who are you aiming this at?

Unfortunately I’d never advocate for something like this at my work. Self-hosting doesn’t make sense in terms of total cost of ownership. I’d rather engineers spent time solving problems in our core business than making sure our wiki is online.

Interesting. My take is more negative than that: modern indicates they are unaware of prior art and the key challenges in the particular space, and consequently the software is buggy and/or fundamentally broken its approach.

I think you should drop “modern” for something like “designed for CI”, “CI-first” or “CI-native”. It’s more informative.

Of all the adjectives to use, why “modern”?

Very common these days. I don’t get it.

This would raise a lot of eyebrows in the UK. Hiring managers might wonder what you’re hiding.

Did you re-apply to the same places with and without those factors? Because if you didn’t, it’s hard to be sure what you’re implying is true.

Alternative explanation: you applied for competitive roles and didn’t hear back because there were better candidates, then you applied for less competitive roles and got interviews.

Agree. Bizarre to use an LLM to do that. I wouldn’t be surprised if the LLM output wasn’t identical to the ORM-generated SQL.

Indeed. Check your employment contract and get legal advice. Your employer might not be thrilled to find out about your side project.

Because enough users find the performance sufficient.

Yeah, we did this. Two big plastic - just polypropylene, I think - boxes under the main sink. One acid and one base.

After daily use for 4 years it felt very routine. Glad I don’t do it anymore though.

I used to love how shiny the glassware was after coming out of the base bath. Lovely!

A colleague of mine used it to etch carbon nanotubes, I think. Absolutely horrible stuff.

When I was a PhD student, my group were given a lab next door to our main lab. During the clear out we found a bottle of HF at the back of a cabinet. Vividly remember us crouching down to look at it all saying “nope, not touching it!”. Department technicians, understandably, weren’t willing to take it off us. In the end I think our PI got the previous lab owner to deal with it.

Like I said, still not seen any evidence.

> ML (particularly DL) tends to outperform "classical" statistical time series forecasting when the data is (strongly) nonlinear, highly dimensional and large.

This claim about forecasting with DL comes up a lot, but I’ve seen little evidence to back it up.

Personally, I’ve never managed to have the same success others apparently have with DL time series forecasting.

Fluidly | London, UK | ONSITE | Data Engineer, Python Engineer, Data Scientist, Full Stack Engineer

We want to solve the biggest problem facing businesses – poor cashflow. We use technology to provide real-time cashflow forecasting and automated credit control.

We recently closed a £5M funding round led by Nyca Partners [1]. We're one of Wired's hottest European startups [2] and have received numerous industry awards. We're hiring across the team, in particular in engineering and data science.

We're a friendly and enthusiastic team using GCP, AWS and Heroku. We're about to go all-in on stream processing on GCP, so we're especially interested in people with experience in streaming. In data science, we use statistical and ML models to forecast time series and solve other problems within business finance.

For more info and to apply visit https://fluidly-london.workable.com.

If you have any questions before applying, feel free to email me at tom.phillips@fluidly.com.

[1]: https://fluidly.com/blog/fluidly-raises-a-5m-series-a-round/ [2]: https://www.wired.co.uk/article/best-startups-in-london-2018

Fluidly | London, UK | ONSITE | Senior and Mid-Level Full Stack Engineers and Senior Data Engineer

We want to solve the biggest problem facing businesses – poor cashflow. We use technology to provide real-time cashflow forecasting and automated credit control. This year we were chosen as one of Wired's hottest European startups and have received numerous industry awards.

We've got an amazing team and great technology stack, mostly running on GCP. It's a great opportunity to scale and build an exciting data-intensive product with machine learning at its core.

For more info and to apply see AngelList: https://angel.co/fluidly/jobs

If you have any questions feel free to email me at tom.phillips@fluidly.com.

Agreed. pipenv is wonderful.

You might be interested in this feature about adults with ADHD: https://www.buzzfeed.com/kellyoakes/these-adults-have-adhd-b...

>To become a chemical engineer, you first have to complete the course work for a degree in chemistry.

That is incorrect.

Look at the programme of study for a chemical engineering undergraduate degree [1]. It doesn't cover anywhere near the same content as an undergraduate chemistry degree [2].

[1]: http://www.imperial.ac.uk/study/ug/courses/chemical-engineer...

[2]: https://www.imperial.ac.uk/study/ug/courses/chemistry-depart...

Those rates are for employees of the college. PhD students aren't employees.

PhD students in the UK typically receive a stipend from their research council. For 2017/18 it's £14,553 [1]. You don't pay national insurance or income tax on a stipend.

I lived in London on such a stipend for four years. It's tough.

[1]: http://www.rcuk.ac.uk/skills/training/

DueDil | Python Developer | London, UK | ONSITE | https://www.duedil.com/careers

DueDil’s mission is to be the largest source of private company data in the world. We want to be the fuel for a more informed and connected economy. This year we launched a new and improved version of our API and became a pan-European platform, covering 100M companies.

We are looking for a Python Developer to join the DueDil Labs team. DueDil Labs works closely with our most valued clients to understand and solve their business problems using private company data.

You will work with two data scientists and our product team to explore ideas, develop prototypes, and build new tools that produce actionable insights from company data. Collaborating with other specialists you will deploy our most successful projects to our web-based enterprise tool and API. You will also write data processing pipelines that onboard new datasets onto our platform.

For more info and to apply see the link on our website. If you have any questions get in touch: tom.phillips@duedil.com

Implementing: never.

Evaluating: occasionally.

In my opinion, an understanding of data structures is _much_ more useful for a data scientist than algorithms.

Why should data scientists know about algorithms? Because data scientists are typically interviewed by computer scientists/software engineers, and that's what they tend to ask.

I recently conducted many phone and on site interviews for a data scientist position. I didn't ask about algorithms.

I'm not making a claim about Python being "best" for algorithms.

The course uses JavaScript. I recommend re-implementing the algorithms covered in the course in another language that you know well. It helped me really understand what's going on.

In my case, I'm most familiar with Python.

This is an excellent course and helped me get my current job.

My background is chemistry/chemical engineering. I had applied for a data scientist position. Phone interview included a problem where I was asked about my solution's complexity. I admitted I didn't know about it.

Still got called back for an interview on site, but the weekend before I powered through this course. Unsurprisingly, it came up in the on-site and they were really pleased I had learnt about it. I got the job.

I also found it useful to implement all the algorithms in Python.