Ritchie uses data and research to explore some of the biggest questions facing society, from climate change and energy to population, public policy and human wellbeing. Her approach is refreshingly straightforward: look beyond headlines and intuition to understand what the numbers actually tell us.

Her writing has a particularly interesting UK focus, with recent articles examining topics such as pension policy, water infrastructure and taxation. Rather than simply arguing for a position, she breaks down the evidence, explains the trade-offs and puts the debate into perspective.

In an age of information overload, By the Numbers is a reminder that good analysis starts with asking the right questions — and being willing to follow the evidence wherever it leads.

Thank you for reading. Reach me for further discussion and updates!

I find the story itself very good, but what made me really love the book is the writing style. The book is written as a sucession of diary entries, letters and magazine news, a style which I just learned is called epistolary, which really adds to the immersion of the reader.

The prose feels authentically Victorian, old and elegant, creating a calm yet dreadful ambiance throughout the whole book. Moreover, some words are written different from nowadays. For instance, today, tomorrow and tonight are written with a dash, i.e., to-day, to-morrow and to-night. I also noticed an intriguing use of the word without in place of what should have been outside. After a quick search, this use of without seems to be archaic nowadays.

Story-wise, I find that the story lacks answers to some questions, for instance:

• If everyone Count Dracula kills turns into an Un-dead, where are the other Un-deads?
• How did Count Dracula originate?

I also find that the book lacks some moral questions from the vampire side, but that is partly answered by clearly distinguishing that the live version and the undead version of people is not the same person.

So overall, I really recommend this book, and if you read it, feel free to start a discussion with me through e-mail or on mastodon, and see you soon!

Thank you for reading. Reach me for further discussion and updates!

In this post, I talk about my experience with blogging and my issues with modern journalism.

For me, personal blogs carry the seal of human-made, organic text. I started blogging because I wanted to write something that inspires people to read only when in a comfortable situation, and not something that be read while commuting, watching television, or worse, while seated on the toilet throne. I wanted people to set apart a time slot, sit comfortably with a hot drink, and enjoy reading one of my posts.

Nowadays, many blogs feel like desperate money-grabbing attempts. For instance, open the r/blogging subreddit (side note: use an alternative frontend to Reddit). Last time I did this, around half of the posts were about whether blogging could still generate money or how to maximise traffic. Many blogs I checked from there were bloated with ads, which really is the easy way to generate money even if your posts are crap.

I hate this. I write my blog for my own satisfaction, for my own pleasure of writing, and I read blogs for the pleasure of reading. A few weeks ago, I read a post about how the Internet felt in the late 1990s and early 2000s. One of the comments which really resonated with me was:

It was like a series of little isolated quirky villages rather than a few massive commercial cities like today.

This threw me back to my own early Internet era, with Habbo Hotel and Club Penguin. In those times, finding a cool website truly felt like a discovery. Today, we're submerged by too much information from too many websites, and I just feel like disconnecting. Except for blogs. Finding a cool personal blog still feels like a discovery. Knowing more about the author, exploring his personality and his activities. Emphatising and linking over similar experiences. It just feels like the old Internet, where people talked to each other.

Generative AI is kind of destroying this, and while I understand using AI to review posts, I just don't bother anymore with blogs that feel 99% AI-generated. After all, why should I bother reading something that nobody bothered to write? On this topic, I refer to Thiery Crouzet's recent posts analysing clanker-generated text with French authors [1] [2].

Modern digital journalism removes all the good sides I cited, and emphasizes further the bad sides: ads, newsletters, paid subscriptions, etc. On this topic, use RSS feeds as much as possible to subscribe to websites [3].

Digital journalism also kills accountability: what people dont realize about physical newspapers is that once it's printed, it can't be changed. The news is out there, and the government can't suppress it. With digital media,it can easily be altered, censored, deleted, and nobody would really know the difference. With my experience in academic research, I think that each blog post should be treated as a scientific article, not in the sense of waiting 2 years before publication, but in the sense that the author should be held accountable, and transparency and sourcing should be a must.

Note: The software Zotero can fix part of those problems: you can use it to read RSS feeds, and you can use it to save snapshots of webpages.

Moreover, and I will stop on this, modern journalism is plagued by right wing propaganda and fake news. A personal blog is the opposite: it serves no greater purpose than displaying a fragment of your personality. I promise this post is only Episode One, and that there will be another one with details and numbers on modern journalism, but for now, I only advise everyone to create his own blog and share it with friends and family.

Thank you for reading. Reach me for further discussion and updates!

The reasons one might want to run an LLM model locally are numerous: no subscription, models can be changed easily thanks to Ollama, data remains private, etc. Thanks to the open source community, you can pretty much do everything you do with online providers, but with a local model on your machine. I identified only two drawbacks. First, you need some skills to set up the network if you want to deploy your models on a different machine than the one you work on; and second, the size of models you can run is greatly limited by the amount of RAM on your machine.

On the latter point, the GX10 features a unified memory between CPU and GPU of 128 GB, which allows me to run models with around 120 billion parameters. However, I found that generation speed is only acceptable for models with less than 35 billion active parameters, regardless of whether they are dense or Mixture of Experts (MoE).

When I first started testing, I had a list of many models I wanted to try, but in the end, I kept only a few for experimentation: – Qwen3.5:27B dense model. – Qwen3.5:122B A10B MoE model. – GLM-4.7-Flash, which is a 30B A3B MoE model.

The objective was to deploy models as intelligent as Sonnet 4.6, and according to artificialanalysis.ai, no model that fits in the GX10 is as intelligent as Sonnet 4.6.

Note: It seems that the recently released Qwen3.8:27B model is similarly intelligent to Sonnet 4.6, although it's far from the latest Sonnet 5.

I ran a series of numeric tests, but I found that the numbers don't really yield meaningful insights. The most important metric is the number of tokens generated per second. This depends on the model and can go anywhere from 10 tokens/sec to 80 tokens/sec. Qwen3.5:27B averaged 12 tokens/sec and Qwen3.5:122B A10B averaged 24 tokens/sec. For a full message, expect somewhere between 30 seconds and a few minutes depending on the input.

I then conducted some conversational testing, although I can only evaluate based on my interpretation of the output messages.

From the three models tested, GLM-4.7-Flash was by far the worst, as it seemed to produce a great deal of hallucinated content. The only difference I saw between the two Qwen models was how each model asked questions to better understand what the user wanted. Qwen3.5:27B seemed closer to discussion, outputting only dry responses, whereas Qwen3.5:122B A10B asked many questions to keep the conversation going.

As for source code, I tried creating two pieces of software, although I must admit those would also be difficult for humans: a Zotero plugin, and a Joplin plugin. Paired with OpenCode, none of the tested models managed to produce a working first version of those plugins. Sonnet 4.6 managed to produce the Zotero one, though it too fell short on the Joplin one.

The main takeaway from these tests is that running LLMs locally takes a lot of time. I find it suitable for conversations where taking a few minutes to answer isn't an issue, but it borders on unusability for tasks such as coding (or vibe coding, as the youngsters call it).

On the price side, I made a simple pessimistic estimate. As for upfront costs, the machine costs at most 4,000 EUR. On the operating costs, the included power supply delivers up to 240 W. This means the machine consumes at most 5.76 kWh per day. At 0.30 EUR per kWh, a pessimistic value in France, this means a cost of at most 1.73 EUR per day, or 52 EUR per month.

With the obvious advantage that there's no usage limit, the investment may be worth it once you exceed the usage limits of the common 20 EUR subscriptions of popular online providers. The main drawback of this is that less and less open-weight models seem to be released nowadays, but given that Qwen3.8 has recently been released, the local-running LLM adventure might not be dead yet.

Thank you for reading. Reach me for further discussion and updates!

But Bon Pote recently published an article remarkably close to the article I had in mind, so, instead of writing my own piece, I'm spotlighting their work.

Bon Pote is a French independent journalism website funded entirely by its readers. It also offers an English version for non-French readers, although it doesn't cover the same articles. Their focus spans climate change and biodiversity collapse, and they always try to strengthen their claims as much as possible by using credible, peer-reviewed resources, thus providing strongly-backed articles.

All articles are public, free, of great quality, and with a computer-generated audio version for most of them. I recommend starting the discovery with two pieces from Sophie Kloetzli: Cars, planes, motorcycles… How noise is slowly killing us (French) and Ban on social media for under-15s: the end of online anonymity? (French). I don't think English versions by the author exist, but you can always translate the whole page with your browser.

Thank you for reading. Reach me for further discussion and updates!

In this month of July, I want to share with you a book that a dear friend lent me: Atomic Habits by James Clear. I know it's not an original recommendation and you often see this book recommended in many places, but perhaps my insights will be original enough to be worth your time.

My initial impression of this book was that it worked by exploiting the placebo effect, but, as we left the introductory chapters to enter the more practical chapters, the book presented many methods to actually improve one's habits.

Note: If you're like me and like your readings to be scientifically sourced, you may find that this book does not have many sources cited in the text. However, there is a long list of sources at the end of the book decomposed by chapter, which you can use along your reading of the book.

The first chapters describe the theory behind habits, and although one can find them trivial, I think the identity part is very important due to the mental shift it induces on the reader, even though it boils down to pure self-motivation. They also explain the decomposition of habits into 4 steps, i.e., cue, craving, response, and reward.

On the more practical side, the first step to integrate a new habit into our lifestyle is to make it obvious. For this, the author recommends maintaining a habit scorecard with all your habits, and stacking new habits with old ones, something he calls habit stacking. I found that, out of the 4 methods given in the book, this is the most impactful. Except for very rare and erratic holidays, I have not failed to execute the new habits I want to integrate in my routine. On this topic, I advise limiting to two new forming habits at a time until they become automatic, which can take up to 90 days.

Another action the author advises to make the habit obvious is to prime your environment, as “motivation is overrated”. The author mainly talks about the physical environment, such as your office or your house, but I suppose that this can also work for your digital environment.

My experience on the environment change is that changing my digital environment had a more meaningful impact than changing my physical environment. I mainly work on my computer, so having my projects and tasklists better organized should have a stronger impact than organizing the physical office I more rarely interact with.

The second step to construct a new habit is to make it attractive. The main action to do this is called temptation bundling. As Mr. Clear says, the strategy is to do the thing you need to do first, then reward yourself with the thing you want to do.

I haven't particularly tested this step, as the new habits I am constructing integrate well with the habit stacking method, and so I don't have any particular feedback on the temptation bundling method for now.

The third step, make it easy, is for me the less useful one, or is at least one I don't really resonate with. The main takeaways I got from this part is that repetition and reduced friction make actions easier, which in turn make picking up new habits much easier.

The last step is to make it satisfying. In this part, Mr. Clear mainly talks about how to ensure you keep a habit streak by suggesting having a habit tracker and having an accountability partner.

For the habit tracker, I cannot explain it better than Mr. Clear himself. As he says, “habit tracking (1) creates a visual cue that can remind you to act, (2) is inherently motivating because you see the progress you are making and don’t want to lose it, and (3) feels satisfying whenever you record another successful instance of your habit.”

An accountability partner is someone with whom you write a habit contract. You promise to execute the habit, and have to do something in return if you do not execute it, such as giving money to the accountability partner for instance. The strategy is to create an immediate cost to not perform a certain habit. To be honest, I'm not a fan of this punishment method at all. However, in the absence of the punishment, an accountability partner can still be useful, especially if it improves your social relationships.

I've written about the four major steps to construct new habits, but throughout the book, Mr. Clear also speaks about how to use the opposite of those steps to remove bad habits. I find the opposite of the third step particularly useful, i.e., making a habit difficult to perform greatly reduces the number of times you do it.

Finally, the last part of the book talks about “advanced tactics” to “go from merely good to being truly great”, and is decomposed in three chapters. I suggest skipping Chapter 18, as I find it to be a defeatist perspective on genetics and “born” talent, although it still has some positive messages. Chapter 19 is a particularly good read on how to deal with the boredom of repeating habits. Chapter 20, the last of the book, enlightens the reader on habit mastery brought by the integration of automated habits and deliberate practice of even the smallest friction points.

Overall, I find Atomic Habits to be a great book, and I recommend it to people who want or need a more methodical lifestyle.

Thank you for reading. Reach me for further discussion and updates!

This post has been brought to you by the “How's this for a work review?” department.

Can mathematical optimisation bridge the gap between last-mile logistics and sustainability? In two months, my contract with KEDGE Business School will be ending, finalising a postdoctoral research of one year in the ConStanCE project. The acronym means, in French, “Contenants Standards pour une Cyclologistique Efficace”.

The subject of the project is the encapsulation of parcels in medium-sized containers for a more efficient modal shift between traditional last-mile logistics and cyclologistics.

Prior to this postdoc, I had never worked in the domain of the supply chain.

Chapter 1: The Discovery

The discovery was quite the challenge, considering I had no real experience with supply chain problems, my main experiences were on more academic problems, and I still had my Ph.D. defense to prepare.

Nonetheless, I think my transition was quite smooth. I quickly learned what the research was about and integrated well in this multiactor project.

The team, CESIT, was nice, and the school building felt really modern compared to the old buildings of the University of Bordeaux.

Chapter 2: The Work

The work was the most difficult part. We were interested in computing, for each studied city, the optimal container capacity, measured in number of parcels, and the corresponding optimal territory design.

Mathematical models

Difficulties concerning this problem included the huge number of clients in the partner LSP's historical data, and thus how to model routing decisions, which are inherently operational decision, into a strategic decision model. We could either use routing algorithms from the literature and wait two millenia for the solution, or approximate the traveling costs via continuous approximations, methods based on demand density instead of accurate customer locations.

We chose the second solution, which gave us a quite small model containing only the container capacity selection constraints, the territory design assignment and contiguity constraints, and some other constraints related to the computation of the objective function.

However, this doesn't mean the problem was easy to solve. In fact, the continuous approximations and some other components of the objective function were non-linear and non-convex / non-concave. At first, I designed some ad-hoc primal and dual heuristic algorithms for the problem to obtain an initial feasible solution and an optimistic value for the objective function.

My objective was to use those heuristic algorithms to warm start exact solvers, and that's were the struggle started. To be honest, SCIP is bad. It crashed on even the simplest instances, and struggle to progress even with a time limit of one hour.

At the ROADEF conference in February 2026, Artelys told me to try Artelys Knitro, their in-house solver for non-linear programming. While it avoided crashes, results were only slightly better.

The breakthrough came when I managed to implement the model using Gurobi, something I had previously failed because, for some reason, it doesn't allow square roots in the objective but it allows a variable v and a constraint v = equation with square roots. Gurobi was able to greatly improve the initial solution in a minute. However, from the first minute to the one hour time limit, it didn't do anything more. Aside from the simplest instances, it never improved the dual bound at all.

Note: A Gurobi webinar highlighted that fixing an initial dual bound via a obj >= z constraint disrupts the variable selection algorithms. Although we didn't really confirm this was the reason, we confirmed that the added constraints did not help the solver.

Cartography tools

I also made a lot of cartography tools using MapBox, but I'm not sure I can display images about those, so I will leave this section for private discussions.

Synthesis and Reports

Finally, one of my favorite parts was writing the feedback reports to the ADEME, the French agency for ecological transition, because this part felt like the conclusion of the different blocks of tasks of the project. So I wrote, a lot, about different subjects:

• Synthesis of current practices of encapsulation for cyclologistics.
• Overview of container management and mutualisation questions for a system with multiple stakeholders.
• Technical report and case study on the capacity sizing and territory designs we developed.

All of those documents will be repackaged into a final report publicly available at the end of 2026. I will update this post once it's online!

Chapter 3: The Future

At the end of this project, I believed less in the potential of cyclologistics. While they offer clea environmental and social benefits in dense urban areas, their scalability is constrained by cargo bike capacities, human operational cycles, and geographic sparsity.

In contrast, I now believe deliveries by autonomous vehicles during nighttime is the better solution, especially with the rising numbers of parcel lockers, which solve the problem of customers being present for the delivery.

Although we already have very promising leads on autonomous vehicles of different sizes for this, the main bottlenecks are regulatory frameworks for sidewalks and multi-agent automatic synchronisation tools.

Key takeaway: I’ll be launching my independent research & optimization consultancy in October 2026. I’m particularly focused on autonomous last-mile logistics, network design, and large-scale mathematical programming, but remain open to projects in sustainable supply chains, operations research, or academic collaborations. Reach out at luismarques@luismarques.me.

Bonus: During this project I presented at two different conferences, ROADEF 2026 and IPIC 2026. I don't have much to say about these, but if you're interested, I can provide you with the submitted abstracts and slides on demand.

Thank you for reading. Reach me for further discussion and updates!

And so I set up a monthly to donation to the French firefighters association, Oeuvre des Pupilles.

Donating to charity, and also to open source projects in particular, is something that had been on my mind for years. In fact, I've long believed that if more people adopted this business model, we could have a more robust open source ecosystem, and thus a safer and better world. However, I must admit that, deep ddown until this point, I still considered it as giving money away “for nothing”. Donating to the firefighters' association was the breakthrough I needed.

I've since set up additional monthly donations to several projects that I find important and interesting: Joplin, KeePassXC, OrganicMaps, and OurWorldInData. In total, I am donating around 40 EUR per month. I call this contributing financial power, but I also contribute brain power (reporting issues, participating in discussions, writing code, etc.) and CPU power (running background tasks on idle devices like my computer when I'm away or my home server). All details are available on my new “Beyond billable hours” page on my website.

The main takeaway of this post is simple: I believe in an open source world where projects can be sustained through voluntary support, and I believe in a world where everyone benefits from those projects regardless of their ability to contribute or finance them. Last words are: small contributions can make a big difference in the life of an open source project, so if you have the means, please support your favorite projects to allow them to live longer.

Thank you for reading. Reach me for further discussion and updates!

This post has been brought to you by the “How's this for an open-source software review?” department.

A few weeks ago, I had another go at sorting my musical library. My workflow for this is quite simple: download the music files, change metadata with MusicBrainz Picard, and use a homemade script to organize musics based on metadata. Playlist management was made using the Strawberry music player.

Note: I have now set up MusicBrainz Picard to organize my library automatically, instead of using my homemade script. I don't know if this is a recent feature, but I don't remember Picard being able to do this before.

Although I'm quite happy with this workflow, there's a major problem that comes before the download step: I don't have a way to track new releases!

You're starting to know me, I'm a big fan of RSS feeds, and a big hater of having an uncountable number of accounts on non-open source websites. However, the most popular and complete services, i.e., Spotify and Deezer, do not provide RSS feeds, and creating an account for this purpose is completely out of question.

I could always use YouTube channel feeds, but artists don't publish all their songs on YouTube, and albums are usually not categorized as releases either, so how can we use RSS (or other open-source software) to get new music releases?

Well, the answer I've found is “nothing much”. There exist a few promising solutions, but they either not work or feel really clunky.

First of all, related to MusicBrainz, there is ListenBrainz. It seems to contain the newest releases per artist, but I didn't find a way to follow each artist independently.

Second, I found a piece of software named Lidarr. Although it seemed good at first, I quickly hit configuration problems because the software is largely overfeatured for what I want.

Finally, I found a website that allows the construction of RSS feeds from Deezer: releasefeed.elomatreb.eu. I see this as a workaround rather than a true solution, but at least it works as I want. I imagine that it polls Deezer website behind the scenes.

Similarly, there exists RSS Bridge that seems to allow to construct RSS feeds from Spotify, but the configuration isn't straightforward. I didn't really experiment with it, so if you have, feel free to reach out and give your feedback!

So to conclude, damn Spotify, Deezer, and other services for not allowing RSS feeds to artists pages (or any other page for that matter).

Thank you for reading. Reach me for further discussion and updates!

This post has been brought to you by the “How's this for a rant?” department.

Today, I only want to make a small rant concerning artificial intelligence and the vocabulary trends I've spotted in social media and company communications, especially on LinkedIn (Fuck LinkedIn by the way).

What's the problem with using “AI” everywhere? The problem is that everything gets categorized in that umbrella category.

In the 2010s, AI was not very known, and it specifically grouped deep and reinforcement learning, two very well known categories of algorithms.

In the 2020s, ChatGPT was released, and the term AI shifted to now group generative text, then generative images and sound algorithms. All of those are now regrouped under the term “GenAI”.

Until here, everything seems fine. The problem appears in the marketing departments of companies. Since those generative algorithms are new, and also quite spectacular in some cases, we often see “AI-driven” products that don't have anything in common with GenAI. This is the case in my field of work, combinatorial optimization, where AI is used vaguely to mention algorithms that we have known since the 1950s!

So, this post is not adressed to the general public, but to the marketing departments that ride on the vague usual definition of AI: stop misleading people with AI-driven products simply because AI makes your spitch shiny. Use the correct terms where they apply.

Thank you for reading. Reach me for further discussion and updates!