Red Memory: The Afterlives of China’s Cultural Revolution

Tania Branigan (2023)

It’s unusual to read a book that probably couldn’t have been written before or after it actually was: the political environment of China thawed just enough to give people confidence to speak to a foreign author about the events of the Cultural Revolution.

The horrors shine through, in the memories of the young Red Guards and their victims. But so too do the current sensitivities, not least in the rather farcical museums and monuments that can be found across China – but that can’t be visited, because the custodians race to close them up “for maintenance” as soon as a visitor arrives. It’s a different approach to that described in Between Two Fires: Truth, Ambition, and Compromise in Putin’s Russia, where museums are re-assigned to new curators who subtly change their message, but it reinforces the fears that authoritarian regimes have of their own histories and those who might actually remember them.

China has never had a public reckoning with the Cultural Revolution – and it doesn’t look that it will have one for a long time. The message of this book is that this hiding of the past continues to do damage to the present, not least because it provides a vague and unexamined fear of disorder that can be used to justify repression “in case it happens again”. But the fact is that it didn’t just “happen”: it was commanded, by a regime that’s the linear predecessor of the current leadership who seem to want to both disown it and levarage it for their own ends.

4/5. Finished Saturday 30 March, 2024.

(Originally published on Goodreads.)

Lisp hackers: Interviews with 100x more productive programmers

Lisp hackers: Interviews with 100x more productive programmers


Vsevolod Dyomkin. Lisp Hackers: Interviews with 100x More Productive Programmers. Leanpub. 2013.

I’m not convinced by the sub-title: at the very least, there’s no evidence to support the claim that Lisp programmers really are hundreds of time more productive. But there are some serious insights here from individuals who’ve made serious contributions to the Lisp world – and beyond. The contributions all take the form of email interviews consisting of a standard prelude of questions (how did you discover Lisp?, do you use it at work?) followed by a couple of questions tailored to the interviewee.

The practitioners selected have all been affected by Lisp, either in terms of it changing their own thought processes, or by helping to develop the core ecosystem, or by applying Lisp to real-world problems, and often to great effect. My favourite was the discussion with someone who was using Common Lisp to develop low-latency network routing for financial services: it’s hard to think of a better example of Lisp’s power and range.

My Lisp experiences and the development of GNU Emacs

My Lisp experiences and the development of GNU Emacs

My Lisp Experiences and the Development of GNU Emacs

The never-dull Richard Stallman talks about his experiences with Lisp and with the development of Emacs. It’s got some useful observations on why Lisp became the core of Emacs: it was news to me that this hadn’t always been the case. It started with an “editor control” language in the spirit of editors like ed. But as people wanted more support from their editor, they wanted to program it more effectively.

The language that you build your extensions on shouldn’t be thought of as a programming language in afterthought; it should be designed as a programming language. In fact, we discovered that the best programming language for that purpose was Lisp.

The result was a re-write of Emacs in C (for portability), with a Lisp interpreter specialised towards editing tasks.

He then gives an insider’s perspective on the Lisp machines and their evolution, as well as revealing the genesis of his ideas for the free software movement. While these are interesting to read, it should be noted that much of it has been rebutted: his description of the formation and later history of the two spin-outs, Symbolics and Lisp Machines Inc, is strongly disputed by Dan Weinreb on his blog.)

The Romanovs: 1613-1918

Simon Sebag Montefiore (2016)

When complimented on his armies capturing Berlin at the end of the Second World War, Stalin famously replied that “Tsar Alexander made it to Paris.” This book describes how, and why, that happened – and why it meant to much to Stalin, on whom Sebag Montefirore is of course a recognised expert.

The sweep of Romanov history is epic in all senses. It’s impossible not to realise how deeply personal their rule was, identifying their own reigns absolutely with Russia and its greatness. The main characters are all flawed in tragic ways, sometimes grotesque but equally often brilliant and self-aware within the limitations of their eras – which none ever really managed to trascend, with the possible exception of Peter and later Catherine, each known as “the Great” for that reason.

The story is made powerful by recent research in the Russian archives and access to previously-unkown letters between Alexander II and his mistress, and later between Nicholas II and Alexandra. They highlight the impact of extra-marital affairs on high policy right across the Romonov era: it’s often hard to keep track of who is related to (or sleeping with) whom, or to understand the true importance of some of the less well-known characters in the story who may have have had an advisory impact far beyond what their “official” position might suggest.

It’s a book that’s hard on autocracy – but also quite hard on the alternatives that came after. Sebag Montefiore sees a continuity between the tsars, Lenin, Stalin, and Putin, and almost seems to regard it as inevitable that Russia will need strongman leadership. One can perhaps hope not.

5/5. Finished Saturday 23 March, 2024.

(Originally published on Goodreads.)

Escape from Model Land: How Mathematical Models Can Lead Us Astray and What We Can Do About It

Erica Thompson (2022)

A thoughtful look at modelling by an experienced climate modeller.

What are models for? The most common answer would be “to predict the future behaviour of some system,” but Thompson argues a far more subtle line: that the most important models often fail to be predictive in any real sense. Much of this is down to problems of validation, especially in climate models for which we have no experience of the world the models are trying to predict.

An even more subtle mistake is regarding all models as “cameras” that simply observe the world. That’s true for the more abstract kinds of modelling, where one is trying to understand possible behaviours of systems in general without tying them to specific circumstances. But the models with which most people are familiar act ore like “engines” that can perturb the system they’re purporting simply to observe by baing used as drivers for policy. Climate and epidemic models seek to warn as well as predict and understand, but this exacerbates the problems of validation: if the model’s predictions don’t come to pass, perhaps this is because policy-makers took corrective actions in response, or maybe bacause they didn’t intervent effectively enough. This isn’t a reason to give up on modelling altogether: how else are we to understand complex systems, and how else are we to respond rationally to them? But it does mean that the notion of “following the science” problematic.

Thompson also wrestles with the problem of groupthink amongst modellers, who often share a common overallping background. I agree this is a problem, but the idea that we can increase diversity in the community easily seems flawed to me. Modellers share a scientific viewpoint and a belief in modelling, and no-one who doesn’t will ever be able to effectively engage with the models or their arguments. Perhaps it’s enough that scientists are always advisors and never decision-makers, and allow politicians to deal with the integration of different choices and values – although that split isn’t always appreciated by the public, and is often (as in the covid-19 pandemic) deliberately blurred to allow less-trusted politicians to draw credibility from more-trusted scientists and doctors.

Overall I think this is a lucid and valiant attempt to summarise and explore the benefits and limitations of models, and science in general, when it impacts directly on the wider world. It deserves to be widely read in the scientific community so that we can better understand our place in policies that we often unavoidably have to influence.

4/5. Finished Friday 22 March, 2024.

(Originally published on Goodreads.)