From f3cf789af7ffa0a7c163da48404038c47b7f7cc7 Mon Sep 17 00:00:00 2001 From: jamesroutley Date: Mon, 23 Dec 2024 22:15:39 +0000 Subject: [PATCH] Build website (automatic) --- docs/index.html | 33 ++--- docs/log.txt | 68 +++++----- ...ths-yet-thoughts-from-a-mathematician.html | 87 ------------ ...se-microplastics-entering-human-cells.html | 29 +--- ...complete-decompilation-of-lego-island.html | 73 ++++++++++ ...ers-which-don-t-take-no-for-an-answer.html | 127 ------------------ ...ational-change-to-cosmological-models.html | 36 +++++ docs/posts/weeknotes-28.html | 2 +- docs/posts/xerox-to-acquire-lexmark.html | 63 --------- 9 files changed, 161 insertions(+), 357 deletions(-) delete mode 100644 docs/posts/can-ai-do-maths-yet-thoughts-from-a-mathematician.html create mode 100644 docs/posts/complete-decompilation-of-lego-island.html delete mode 100644 docs/posts/feed-readers-which-don-t-take-no-for-an-answer.html create mode 100644 docs/posts/supernovae-evidence-for-foundational-change-to-cosmological-models.html delete mode 100644 docs/posts/xerox-to-acquire-lexmark.html diff --git a/docs/index.html b/docs/index.html index 051aa92da73..8d3a51e4ba7 100644 --- a/docs/index.html +++ b/docs/index.html @@ -16,18 +16,17 @@

News

+
  • - The journey to save the last known 43-inch Sony CRT (obsoletesony.substack.com) + Show HN: Complete decompilation of Lego Island (github.com)
    • - -
  • - Why HNSW is not the answer and disk-based alternatives might be more practical (blog.pgvecto.rs) + The journey to save the last known 43-inch Sony CRT (obsoletesony.substack.com)
    • @@ -35,8 +34,9 @@

    News

    +
  • - Show HN: Llama 3.3 70B Sparse Autoencoders with API access (www.goodfire.ai) + Why HNSW is not the answer and disk-based alternatives might be more practical (blog.pgvecto.rs)
    • @@ -45,7 +45,7 @@

    News

  • - Fogus: Things and Stuff of 2024 (blog.fogus.me) + Show HN: Llama 3.3 70B Sparse Autoencoders with API access (www.goodfire.ai)
    • @@ -54,7 +54,7 @@

    News

  • - Show HN: Keypub.sh – OAuth for the terminal using SSH keys (keypub.sh) + Supernovae Evidence for Foundational Change to Cosmological Models (arxiv.org)
    • @@ -63,7 +63,7 @@

    News

  • - Commercial tea bags release microplastics, entering human cells (medicalxpress.com) + Fogus: Things and Stuff of 2024 (blog.fogus.me)
    • @@ -72,7 +72,7 @@

    News

  • - The intricacies of implementing memoization in Ruby (denisdefreyne.com) + Show HN: Keypub.sh – OAuth for the terminal using SSH keys (keypub.sh)
    • @@ -81,7 +81,7 @@

    News

  • - My Colleague Julius (ploum.net) + Commercial tea bags release microplastics, entering human cells (medicalxpress.com)
    • @@ -90,7 +90,7 @@

    News

  • - Xerox to acquire Lexmark (newsroom.lexmark.com) + The intricacies of implementing memoization in Ruby (denisdefreyne.com)
    • @@ -99,7 +99,7 @@

    News

  • - Can AI do maths yet? Thoughts from a mathematician (xenaproject.wordpress.com) + My Colleague Julius (ploum.net)
    • @@ -386,15 +386,6 @@

    News

    -
  • - Feed readers which don't take "no" for an answer (rachelbythebay.com) -
    • - - - - - -
  • Practical checks when working with alembic migrations (ldirer.com)
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    Can AI do maths yet? Thoughts from a mathematician

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    So the big news this week is that o3, OpenAI’s new language model, got 25% on FrontierMath. Let’s start by explaining what this means.

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    What is o3? What is FrontierMath?

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    A language model, as probably most people know, is one of these things like ChatGPT where you can ask it a question and it will write some sentences which are an attempt to give you an answer. There were language models before ChatGPT, and on the whole they couldn’t even write coherent sentences and paragraphs. ChatGPT was really the first public model which was coherent. There have been many other models since. Right now they’re still getting better really fast. How much longer this will go on for nobody knows, but there are lots of people pouring lots of money into this game so it would be a fool who bets on progress slowing down any time soon. o3 is a new language model.

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    FrontierMath is a secret dataset of “hundreds” of hard maths questions, curated by Epoch AI, and announced last month. “Hundreds” is a quote from the paper (first line of the abstract), but I’ve heard a rumour that when the paper came out there were under 200 questions, although I’ve heard another rumour that apparently more are have been added since. As an academic mathematician who spent their entire life collaborating openly on research problems and sharing my ideas with other people, it frustrates me a little that already in this paragraph we’ve seen more questions than answers — I am not even to give you a coherent description of some basic facts about this dataset, for example, its size. However there is a good reason for the secrecy. Language models train on large databases of knowledge, so the moment you make a database of maths questions public, the language models will train on it. And then if you ask such a model a question from the database they’ll probably just rattle off the answer which they already saw.

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    How hard is the FrontierMath dataset?

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    So what are the questions in the FrontierMath dataset like? Here’s what we know. They’re not “prove this theorem!” questions, they’re “find this number!” questions. More precisely, the paper says “Problems had to possess definitive, computable answers that could be automatically verified”, and in the five sample problems which were made public from the dataset (Appendix A of the paper, pages 14 to 23) the solutions are all positive whole numbers (one answer is 9811, another is 367707, and the final three solutions are even larger — clearly these questions are designed in such a way that random guesswork is extremely unlikely to succeed). The sample questions are nontrivial, even to a research mathematician. I understood the statements of all five questions. I could do the third one relatively quickly (I had seen the trick before that the function mapping a natural n to alpha^n was p-adically continuous in n iff the p-adic valuation of alpha-1 was positive) and I knew exactly how to do the 5th one (it’s a standard trick involving the Weil conjectures for curves) but I didn’t bother doing the algebra to work out the exact 13-digit answer. The first and second question I knew I couldn’t do, and I figured I might be able to make progress on the 4th one if I put some real effort in, but ultimately I didn’t attempt it, I just read the solution. I suspect that a typical smart mathematics undergraduate would struggle to do even one of these questions. To do the first one you would, I imagine, have to be at least a PhD student in analytic number theory. The FrontierMath paper contains some quotes from mathematicians about the difficulty level of the problems. Tao (Fields Medal) says “These are extremely challenging” and suggests that they can only be tackled by a domain expert (and indeed the two sample questions which I could solve are in arithmetic, my area of expertise; I failed to do all of the ones outside my area). Borcherds (also Fields Medal) however is quoted in the paper as saying that machines producing numerical answers “aren’t quite the same as coming up with original proofs”.

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    So why make such a dataset? The problem is that grading solutions to “hundreds” of answers to “prove this theorem!” questions is expensive (one would not trust a machine to do grading at this level, at least in 2024, so one would have to pay human experts), whereas checking whether hundreds of numbers in one list correspond to hundreds of numbers in another list can be done in a fraction of a second by a computer. As Borcherds pointed out, mathematics researchers spend most of the time trying to come up with proofs or ideas, rather than numbers, however the FrontierMath dataset is still extremely valuable because the area of AI for mathematics is desperately short of hard datasets, and creating a dataset such as this is very hard work (or equivalently very expensive). This recent article by Frieder et al talks in a lot more depth about the shortcomings in datasets for AI in mathematics.

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    So there was an article about the FrontierMath dataset in Science and I was quoted in it as saying “If you have a system that can ace that database, then it’s game over for mathematicians.” Just to be clear: I had nothing to do with the dataset, I’ve only seen the five public questions, and was basing my comments on those. I also said “In my opinion, currently, AI is a long way away from being able to do those questions … but I’ve been wrong before”. And then this week there’s an announcement that the language model o3 got a score of 25 percent on the dataset. I was shocked.

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    What exactly has happened here?

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    Why was I shocked? Because my mental model on where “AI” is currently, when it comes to doing mathematics, is “undergrad or pre-undergrad”. It’s getting very good at “Olympiad-style” problems of the sort given to bright high-schoolers. Within a year it’s absolutely clear that AI systems will be passing undergraduate mathematics exams (not least because when you’re setting an undergraduate mathematics exam you ideally need to make sure that you don’t fail 50 percent of the class, so you throw in a couple of standard questions which are very similar to questions that the students have seen already, to ensure that those with a basic understanding of the course will pass the exam. Machines will easily be able to ace such questions). But the jump from that to having innovative ideas at advanced undergrad/early PhD level beyond recycling standard ideas seems to me to be quite a big one. For example I was very unimpressed by the ChatGPT answers to the recent Putnam exam posted here — as far as I can see only question B4 was answered adequately by the machine, most other answers are worth one or two out of 10 at most. So I was expecting this dataset to remain pretty unattackable for a couple of years.

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    My initial excitement was tempered however by a post from Elliot Glazer from Epoch AI on Reddit where he claimed that in fact 25 percent of the problems in the dataset were “IMO/undergrad style problems”. This claim is a little confusing because I would be hard pressed to apply such adjectives to any of the five publically-released problems in the dataset; even the simplest one used the Weil conjectures for curves (or a brute force argument which is probably just about possible but would be extremely painful, as it involves factoring 10^12 degree 3 polynomials over a finite field, although this could certainly be parallelised). This of course raises questions in my mind about what the actual level of the problems in this secret dataset is (or equivalently whether the five public questions are actually a representative sample), but this is not knowledge which we’re likely to have access to. Given this new piece of information that 25 percent of the problems are undergraduate level, perhaps I will revert to being unsurprised again, but will look forward to being surprised when AI is getting nearer 50 percent on the dataset, because performance at “qual level” (as Elliot describes it — the next 50 percent of the questions) is exactly what I’m waiting to see from these systems — for me this would represent a big breakthrough.

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    Prove this theorem!

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    However, as Borcherds points out, even if we ended up with a machine which was super-human at “find this number!” questions, it would still have limited applicability in many areas of research mathematics, where the key question of interest is usually how to “prove this theorem!”. In my mind, the biggest success story in 2024 is DeepMind’s AlphaProof, which solved four out of the six 2024 IMO (International Mathematics Olympiad) problems. These were either “prove this theorem!” or “find a number and furthermore prove that it’s the right number” questions and for three of them, the output of the machine was a fully formalized Lean proof. Lean is an interactive theorem prover with a solid mathematics library mathlib containing many of the techniques needed to solve IMO problems and a lot more besides; DeepMind’s system’s solutions were human-checked and verified to be “full marks” solutions. However, we are back at high school level again; whilst the questions are extremely hard, the solutions use only school-level techniques. In 2025 I’m sure we’ll see machines performing at gold level standard in the IMO. However this now forces us to open up the “grading” can of worms which I’ve already mentioned once, and I’ll finish this post by talking a little more about it.

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    Who is marking the machines?

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    July 2025. I can envisage the following situation. As well as hundreds of the world’s smartest schoolchildren entering the IMO, there will be machines entering. Hopefully not too many though. Because the systems will be of two types. There will be systems submitting answers in the language of a computer proof checker like Lean (or Rocq, Isabelle, or many others). And there will be language models submitting answers in human language. The big difference between these two submissions are that: if a marker verifies that the statement of the question has been correctly translated into the computer proof checker, then all they need to do is to check that the proof compiles and then they basically know that it is a “full marks” solution. For the language models we will have a situation like the poor Putnam solutions above — the computer will write something, it will look convincing, but a human is going to have to read it carefully and grade it, and there is certainly no guarantee that it will be a “full marks” solution. Borcherds is right to remind the AI community that “prove this theorem!” is what we really want to see as mathematicians, and language models are currently at least an order of magnitude less accurate than expert humans when it comes to logical reasoning. I am dreading the inevitable onslaught in a year or two of language model “proofs” of the Riemann hypothesis which will just contain claims which are vague or inaccurate in the middle of 10 pages of correct mathematics which the human will have to wade through to find the line which doesn’t hold up. On the other hand, theorem provers are at least an order of magnitude more accurate: every time I’ve seen Lean not accept a human argument in the mathematical literature, the human has been wrong.

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    In fact, as mathematicians, we would like to see more than “prove this theorem!”. We would like to see “prove this theorem, correctly, and explain what makes the proof work in a way which we humans understand”. With the language model approach I worry (a lot) about “correctly” and with the theorem prover approach I worry about “in a way which we humans understand”. There is still a huge amount to be done. Progress is currently happening really quickly. But we are a long way away. When will we “beat the undergraduate barrier”? Nobody knows.

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    - - diff --git a/docs/posts/commercial-tea-bags-release-microplastics-entering-human-cells.html b/docs/posts/commercial-tea-bags-release-microplastics-entering-human-cells.html index 25d510e7b85..6d0ff3d9b2f 100644 --- a/docs/posts/commercial-tea-bags-release-microplastics-entering-human-cells.html +++ b/docs/posts/commercial-tea-bags-release-microplastics-entering-human-cells.html @@ -31,10 +31,7 @@

    Commercial tea bags release microplastics, entering human cells

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    Plastic waste pollution represents a critical environmental challenge with increasing implications for the well-being and health of future generations. Food packaging is a major source of micro and nanoplastic (MNPLs) contamination and inhalation and ingestion is the main route of human exposure.

    A study by the Mutagenesis Group of the UAB Department of Genetics and Microbiology has successfully obtained and characterized micro and nanoplastics derived from several types of commercially available tea bags. The paper is published in the journal Chemosphere.

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    Commercial tea bags release microplastics, entering human cells

    To characterize the different types of particles present in the infusion, a set of advanced analytical techniques such as scanning (SEM), (TEM), (ATR-FTIR), dynamic light scattering (DLS), laser Doppler velocimetry (LDV), and nanoparticle tracking analysis (NTA) were used.

    "We have managed to innovatively characterize these pollutants with a set of cutting-edge techniques, which is a very important tool to advance research on their possible impacts on human health," said UAB researcher Alba Garcia.

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    Interactions with human cells observed for the first time

    The particles were stained and exposed for the first time to different types of to assess their interaction and possible cellular internalization. The biological interaction experiments showed that mucus-producing intestinal cells had the highest uptake of micro and nanoplastics, with the particles even entering the cell nucleus that houses the genetic material.

    The result suggests a key role for intestinal mucus in the uptake of these pollutant particles and underscores the need for further research into the effects that chronic exposure can have on human health.

    @@ -57,25 +51,10 @@

    Interactions with human cells observed for the first time

    More information: Gooya Banaei et al, Teabag-derived micro/nanoplastics (true-to-life MNPLs) as a surrogate for real-life exposure scenarios, Chemosphere (2024). DOI: 10.1016/j.chemosphere.2024.143736 -

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    Journal information: - Chemosphere

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    - Provided by - Autonomous University of Barcelona

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    diff --git a/docs/posts/complete-decompilation-of-lego-island.html b/docs/posts/complete-decompilation-of-lego-island.html new file mode 100644 index 00000000000..7bb47d6f26a --- /dev/null +++ b/docs/posts/complete-decompilation-of-lego-island.html @@ -0,0 +1,73 @@ + + + + + + + James Routley | Feed + + + + Back + Original +

    Show HN: Complete decompilation of Lego Island

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    Development Vlog | Contributing | Matrix | Forums | Patreon

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    This is a functionally complete decompilation of LEGO Island (Version 1.1, English). It aims to be as accurate as possible, matching the recompiled instructions to the original machine code as much as possible. The goal is to provide a workable codebase that can be modified, improved, and ported to other platforms later on.

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    Both ISLE.EXE and LEGO1.DLL are completely decompiled and, to the best of our knowledge, are functionally identical to the originals. However, work is still ongoing to improve the accuracy, naming, documentation, and structure of the source code. While there may still be unresolved bugs that are not present in retail, the game should be fully playable with the binaries derived from this source code.

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    Due to various complexities with regard to the compiler, these binaries are not a byte-for-byte match of the original executables. We remain hopeful that this can be resolved at some point.

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    This project uses the CMake build system, which allows for a high degree of versatility regarding compilers and development environments. For the most accurate results, it is recommended to use Microsoft Visual C++ 4.20 (the same compiler used to build the original game). Since we're trying to match the output of this code to the original executables as closely as possible, all contributions will be graded with the output of this compiler.

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    These instructions will outline how to compile this repository using Visual C++ 4.2 into highly-accurate binaries where the majority of functions are instruction-matching with retail. If you wish, you can try using other compilers, but this is at your own risk and won't be covered in this guide.

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    You will need the following software installed:

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    • Microsoft Visual C++ 4.2. This can be found on many abandonware sites, but the installer can be a little iffy on modern versions of Windows. For convenience, a portable version is available that can be downloaded and used quickly instead.
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    • CMake. A copy is often included with the "Desktop development with C++" workload in newer versions of Visual Studio; however, it can also be installed as a standalone app.
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    1. Open a Command Prompt (cmd).
      2. +
    2. From Visual C++ 4.2, run BIN/VCVARS32.BAT x86 to populate the path and other environment variables for compiling with MSVC.
      3. +
    3. Make a folder for compiled objects to go, such as a build folder inside the source repository (the folder you cloned/downloaded to).
      4. +
    4. In your Command Prompt, cd to the build folder.
      5. +
    5. Configure the project with CMake by running:
      6. +
    +
    cmake <path-to-source> -G "NMake Makefiles" -DCMAKE_BUILD_TYPE=RelWithDebInfo
+
    +
      +
    • Visual C++ 4.2 has issues with paths containing spaces. If you get configure or build errors, make sure neither CMake, the repository, nor Visual C++ 4.2 is in a path that contains spaces.
      • +
    • Replace <path-to-source> with the source repository. This can be .. if your build folder is inside the source repository.
      • +
    • RelWithDebInfo is recommended because it will produce debug symbols useful for further decompilation work. However, you can change this to Release if you don't need them. Debug builds are not recommended because they are unlikely to be compatible with the retail LEGO1.DLL, which is currently the only way to use this decompilation for gameplay.
      • +
    • NMake Makefiles is most recommended because it will be immediately compatible with Visual C++ 4.2. For faster builds, you can use Ninja (if you have it installed), however due to limitations in Visual C++ 4.2, you can only build Release builds this way (debug symbols cannot be generated with Ninja).
      • +
    +
      +
    1. Build the project by running nmake or cmake --build <build-folder>
      2. +
    2. When this is done, there should a recompiled ISLE.EXE and LEGO1.DLL in the build folder.
      3. +
    3. Note that nmake must be run twice under certain conditions, so it is advisable to always (re-)compile using nmake && nmake.
      4. +
    +

    If you have a CMake-compatible IDE, it should be pretty straightforward to use this repository, as long as you can use VCVARS32.BAT and set the generator to NMake Makefiles.

    + +

    Simply place the compiled ISLE.EXE and LEGO1.DLL into LEGO Island's install folder (usually C:\Program Files\LEGO Island or C:\Program Files (x86)\LEGO Island). Alternatively, LEGO Island can run from any directory as long as ISLE.EXE and LEGO1.DLL are in the same directory, and the registry keys (usually HKEY_LOCAL_MACHINE\Software\Mindscape\LEGO Island or HKEY_LOCAL_MACHINE\Software\Wow6432Node\Mindscape\LEGO Island) point to the correct location for the asset files.

    + +

    If you're interested in helping or contributing to this project, check out the CONTRIBUTING page.

    + +

    Which version of LEGO Island do I have?

    +

    Right click on LEGO1.DLL, select Properties, and switch to the Details tab. Under Version you should either see 1.0.0.0 (1.0) or 1.1.0.0 (1.1). Additionally, you can look at the game disc files; 1.0's files will all say August 8, 1997, and 1.1's files will all say September 8, 1997. Version 1.1 is by far the most common, especially if you're not using the English or Japanese versions, so that's most likely the version you have.

    +

    Please note that some localized versions of LEGO Island were recompiled with small changes despite maintaining a version number parallel with other versions; this decompilation is specifically targeting the English release of version 1.1 of LEGO Island. You can verify you have the correct version using the checksums below:

    +
      +
    • ISLE.EXE md5: f6da12249e03eed1c74810cd23beb9f5
      • +
    • LEGO1.DLL md5: 4e2f6d969ea2ef8655ba3fc221a0c8fe
      • +
    • CONFIG.EXE md5: 92d958a64a273662c591c88b09100f4a
      • +
    +
    + + diff --git a/docs/posts/feed-readers-which-don-t-take-no-for-an-answer.html b/docs/posts/feed-readers-which-don-t-take-no-for-an-answer.html deleted file mode 100644 index ac88fbe591a..00000000000 --- a/docs/posts/feed-readers-which-don-t-take-no-for-an-answer.html +++ /dev/null @@ -1,127 +0,0 @@ - - - - - - - James Routley | Feed - - - - Back - Original -

    Feed readers which don't take "no" for an answer

    - - -

    -I don't think people really appreciate what kind of mayhem some of their -software gets up to. I got a bit of feedback the other night from -someone who's been confounded by the site becoming unreachable. Based -on running traceroutes, this person thinks that maybe it's carrier A or -carrier B, or maybe even my own colocation host. -

    -

    -I would have responded to this person directly, but they didn't leave -any contact info, so all I can do is write a post and hope it reaches -them and others in the same situation. -

    -

    -It's not any of the carriers and it's not Hurricane Electric. It's my -end, and it's not an accident. Hosts that get auto-filtered are -usually running some kind of feed reader that flies in the face of best -practices, and then annoys the web server, receives 429s, and then -ignores those and keeps on going. -

    -

    -The web server does its own thing. I'm not even in the loop. I can be -asleep and otherwise entirely offline and it'll just chug along without -me. -

    -

    -A typical timeline goes like this: -

    - -

    -Somewhere around here, the web server decided that it wasn't being -listened to, and so it decided it was going to stop listening, too. -

    -

    -Some time after this, it will "forgive" and then things will work again, -but of course, if there's still a bad feed reader running out there, it -will eventually start this process all over again. -

    -

    -A 20 minute retry rate with unconditional requests is wasteful. That's -three requests per hour, so 72 requests per day. That'd be about 36 MB -of traffic that's completely useless because it would be the same feed -contents over and over and over. -

    -

    -Multiply that by a bunch of people because it's a popular feed, and that -should explain why I've been tilting at this windmill for a while now. -

    -

    -If you're running a feed reader and want to know what its behavior looks -like, the "feed reader score" project thing I set up earlier this year -is still running, and is just humming along, logging data as always. -

    -

    -You just point your reader at a special personalized URL, and you will -receive a feed with zero nutritional content but many of your reader's -behaviors (*) will be analyzed and made available in a report page. -

    -

    -It's easy... and I'm not even charging for it. (Maybe I should?) -

    -

    -... -

    -

    -(*) I say _many_ of the behaviors since a bunch of these things have -proven that my approach of just handing people a bunch of uniquely-keyed -paths on the same host is not nearly enough. Some of these feed readers -just go and make up their own paths and that's garbage, but it also -means my dumb little CGI program at /one/particular/path doesn't see it. -It also means that when they drill / or /favicon.ico or whatever, it -doesn't see it. I can't possibly predict all of their clownery, and -need a much bigger hammer. -

    -

    -There's clearly a -Second System -waiting to be written here. -

    -

    -As usual, the requirements become known after you start doing the thing. -

    - - - diff --git a/docs/posts/supernovae-evidence-for-foundational-change-to-cosmological-models.html b/docs/posts/supernovae-evidence-for-foundational-change-to-cosmological-models.html new file mode 100644 index 00000000000..3f9f0765d8d --- /dev/null +++ b/docs/posts/supernovae-evidence-for-foundational-change-to-cosmological-models.html @@ -0,0 +1,36 @@ + + + + + + + James Routley | Feed + + + + Back + Original +

    Supernovae Evidence for Foundational Change to Cosmological Models

    + +
    +
    + + + +

    View PDF + HTML (experimental)

    + Abstract:We present a new, cosmologically model-independent, statistical analysis of the Pantheon+ type Ia supernovae spectroscopic dataset, improving a standard methodology adopted by Lane et al. We use the Tripp equation for supernova standardisation alone, thereby avoiding any potential correlation in the stretch and colour distributions. We compare the standard homogeneous cosmological model, i.e., $\Lambda$CDM, and the timescape cosmology which invokes backreaction of inhomogeneities. Timescape, while statistically homogeneous and isotropic, departs from average Friedmann-Lema\^ıtre-Robertson-Walker evolution, and replaces dark energy by kinetic gravitational energy and its gradients, in explaining independent cosmological observations. When considering the entire Pantheon+ sample, we find very strong evidence ($\ln B> 5$) in favour of timescape over $\Lambda$CDM. Furthermore, even restricting the sample to redshifts beyond any conventional scale of statistical homogeneity, $z > 0.075$, timescape is preferred over $\Lambda$CDM with $\ln B> 1$. These results provide evidence for a need to revisit the foundations of theoretical and observational cosmology. +
    + + + +
    +
    +

    Submission history

    From: Zachary Lane [view email]

    + + diff --git a/docs/posts/weeknotes-28.html b/docs/posts/weeknotes-28.html index 8c417007d5a..0fa8ff47bad 100644 --- a/docs/posts/weeknotes-28.html +++ b/docs/posts/weeknotes-28.html @@ -68,7 +68,7 @@

    Weeknotes #28

  • i feel like i'm really clear on what i want this year without a big chunk of planning, but i also want conflicting things e.g. fancy sleeve tattoo vs. saving a big emergency fund.
  • since i guess i won't write another thing on here weeknotes wise till after xmas i hope you all have a fantastic one if you celebrate, and a great day otherwise too! happy solstice for tomorrow, even if you don't celebrate it too (brighter days are coming, northern hemisphere! the hateful sun is going back in her cage, southern hemisphere!) xo
    • -

    Last updated 2 days, 21 hours ago

    +

    Last updated 2 days, 22 hours ago

    If you liked this post, please message, email, or follow me online, check out my work in progress, share this post or subscribe to my posts by RSS!

    diff --git a/docs/posts/xerox-to-acquire-lexmark.html b/docs/posts/xerox-to-acquire-lexmark.html deleted file mode 100644 index b2ad289c3e4..00000000000 --- a/docs/posts/xerox-to-acquire-lexmark.html +++ /dev/null @@ -1,63 +0,0 @@ - - - - - - - James Routley | Feed - - - - Back - Original -

    Xerox to acquire Lexmark

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