jonmoore

This appears to be ai-generated based on my impression, pangram's "100% of this text is ai", and the blog's posting history.

Yes, utter slop. Hacker News could do with support for flagging and ignoring such submissions.

Not “instead of” but “in addition to”. Government regulation is not perfect but is the first-best solution to imposing such injunctions, and prima facie is much more effective than social norms or industry self-regulation. That’s a major part of why the industry bodies are objecting to it.

This would be much more convincing if the repos, issue trackers, etc. were accessible.

Life on Earth was hugely ambitious in ideas too, covering the whole history of life, with evolution as an underpinning theory throughout. As a child, it had much more impact on me than anything else on TV, and I still remember a lot of scenes.

I really liked the evaluation method here - testing fidelity by round-tripping through chains of invertible steps. It was striking how even frontier models accumulated errors on seemingly computer-friendly tasks.

It would be interesting to know if the stronger results on Python are not just an artefact of the Python-specific evaluation, if they carry over to other common general-purpose languages, and if they are driven by something specific in the training processes.

Indeed. This is just a vibe-coded addition to an already overcrowded space, with no indication of any intent to consult others or support it for real use.

The VLDB paper mentioned is https://www.vldb.org/pvldb/vol16/p1601-budiu.pdf.

Abstract:

"Incremental view maintenance has been for a long time a central problem in database theory. Many solutions have been proposed for restricted classes of database languages, such as the relational algebra, or Datalog. These techniques do not naturally generalize to richer languages. In this paper we give a general solution to this problem in 3 steps: (1) we describe a simple but expressive language called DBSP for describing computations over data streams; (2) we give a general algorithm for solving the incremental view maintenance problem for arbitrary DBSP programs, and (3) we show how to model many rich database query languages (including the full relational queries, grouping and aggregation, monotonic and non-monotonic recursion, and streaming aggregation) using DBSP. As a consequence, we obtain efficient incremental view maintenance techniques for all these rich languages."

I asked the same question to one of the core devs at a recent event and he (1) said that some people in finance have done related things and (2) suggested using the Ray slack to connect with developers and power users who might have helpful advice.

I agree this is a very interesting area to consider Ray for. There are lots of projects/products that provide core components that could be used but there’s no widely used library. It feels like one is overdue.

I also like Racket for a good few of these reasons, but the tooling has a lot of sharp edges and limitations in practice. It would be unfair to expect a full JetBrains/Microsoft IDE experience but it surprised me that a Lisp descendant would come with such an underpowered default REPL. I also find, whatever the technical merits of Scribble compared to docstrings and Markdown, that the documentation for most Racket packages is poorly written compared to that for even minor packages in common languages.

Thanks. This focuses on the most common daily need, with really clean design and display of information, and live updates as a flourish. Definitely sparks joy.

I can be pretty cynical about corporate politics but that kind of consultant-IC interview is almost always safe. It’s wildly against the consultant’s interests to cause serious conflict to break out and it’s easy to tone down inflammatory comments. Also, if the IC’s ideas are ones likely to rile up management the consultant is going to drop them. There are also potential upsides; if senior management is decent they will be glad that their employees have good suggestions and a good fraction of consultants will be glad to credit the ICs once management have bought in; ofc some will take all the credit themselves but that’s still a low-downside proposition.

The Modern Data Stack / MLOps product space was succinctly described by one actually-technical CEO as "vending into ignorance"; the author corroborates this with a commendably candid take:

>Imagine it’s 2021, peak MDS, and you meet the CDO of a large bank. “Oh cool,” she says, “you’re the CEO of a tech company. What does your product do?” What do you say?

>“We build a tool that leverages the power of the cloud to apply standard SQL and software engineering best practices to the historically mundane (but critical!) job of data transformation.”

>“We’re the standard for data transformation in the modern data stack.”

>I will tell you that, empirically, option #2 is more effective.

This tallies with what I've seen from a lot of enterprise CxOs and their teams as technology hype moved from big data and block chain and onto data science/machine learning.

There is so much to write about this, but I'll just recommend "Life Cycle of a Silver Bullet" http://freyr.websages.com/Life_Cycle_of_a_Silver_Bullet.pdf, which deserves more attention than it's had on HN.

Indeed, and tools like dep-tree provide a combination of 1) making module structure visible 2) making rules about this structure concrete and 3) automatically checking for rule violations.

These all help to lower the cognitive barrier to learning and maintaining the code base effectively. For developers new to the code base they help with learning and for those more experienced they help with ongoing design and maintenance.

Most long-lived code bases I've seen have adopted or built such tooling at some point, often with tools customized to the code base. For example in one large code base (c. 250 devs) we built tooling that simulated and helped optimize the changes to implement a major refactor of the overall module structure.

> If I say "Dyson" and you're in the UK, you think of

Mathematical Physics :)

There is a more general algorithm called Delta Debugging (https://en.m.wikipedia.org/wiki/Delta_debugging), introduced in Andreas Zeller’s 1999 paper "Yesterday, my program worked. Today, it does not. Why?" (https://doi.org/10.1145/318774.318946), recommended if you’re interested in the topic.

Coincidentally, today I noticed a surprisingly high number of file accesses from Tenable's Nessus software, caused by it reading a megabyte-sized config file one character at a time without buffering, each going through Win32's ReadFile.

It seems that negligence is not in short supply.

In this case Chotiner didn’t come back at weak arguments as much as he often does. In particular Frank downplayed the importance of stress tests, arguing variously that they are procedural, mostly about contagion, don’t concern the “physical”, i.e. capital, condition of banks, and wouldn’t have helped because “this all came up very suddenly”. This is all incorrect.

Likewise when Frank argues in favor of his working for the banking industry, this raises conflicts of interests for legislators and potential for undue and hard-to-detect influence in a way that e.g. taking an academic position concerning banking or lobbying for gay rights would not.

Toolz, like seemingly everything Matt Rocklin is a major contributor to, is something of a model library: cleanly designed and coded, with strong documentation.

Although Python is not going to match a full Lisp, Haskell or ML in all their strengths, using a functional style can be useful and expressive. The toolz docs give some relevant background at https://toolz.readthedocs.io/en/latest/heritage.html .

At a language level, Peter Norvig gave a lengthy comparison of Python and Lisp at https://norvig.com/python-lisp.html in 2000.

A bypass provides a route around a population center to offload traffic - this would have done the opposite, reducing quality of life for thousands of nearby residents, workers, and others using the neighborhood.

For comparison, the more recent approach of increased pedestrianisation and traffic calming has been a significant improvement to life in the New York areas where it's been applied.

As a pure means of communication the LANL preprint server, which later became arxiv, made the journals in its core areas of theoretical physics obsolete basically overnight, so vastly superior was it in terms of speed of distribution and ease of access.

To me it’s one of the high points of the web and Paul Ginsparg is an absolute hero for setting it up; he reviews some of its history at https://arxiv.org/abs/1108.2700.

I think it’s an interesting social question why other fields have taken so much longer to adopt the arxiv model.

Very nice work. I especially liked the ability to build up a collection of papers, that the response time was good, and that the SQL could be edited directly.

Do you have any plans to add a graphical visualization of top/central papers?