Next.js Updated 2025-07-16
Framework built on top of React.
gothinkster/realworld blog example by Ciro Santilli: node Express Sequelize Next.js realworld example app.
Basically what this does is to get server-side rendering just working by React, including hydration, which is a good thing.
Next.js sends the first pre-rendered HTML page along with the JavaScript code. Then, JavaScript page switches just load the API data.
Next.js does this nicely by forcing you to provide page data in a serialized JSON format, even when rendering server-side (e.g. the return value of
getServerSideProps). This way, it is also able to provide either the full HTML, or just the JSON.Some general downsides:
- it does feel like they don't document deployment very well however, especially non-Vercel options, which is the company behind Next.js. I'm unable to find how to use a non Vercel CDN with ISR supposing that is possible.
- Next.js is very opinionated, and like any opinionated library it is sometimes hard to know why something is/isn't happening, and sometimes it is hard/impossible to do what you want with it unless they add support. They have done good progress, but even as of 2022, some aspects just feel so immature, some major-looking use cases are not very well done.
In theory, Next.js could be the "ultimate frontend framework". It does have a lot of development difficulties that need to be ironed out, but the general concepts, and things it tries to integrate, including e.g. webpack, TypeScript, etc. are good. Maybe the question is when will someone put it together with an amazing backend library and dominate and finally put an end to the infinite number of Js Frameworks!
In-tree examples at: github.com/vercel/next.js/tree/canary/examples
In order to offer its amazing features, Next.js is also extremely opinionated, which means that if something wasn't designed to be possible, it basically isn't.
No prerender with custom server? It forces you to write your API with next as well? Or does it mean something else?
TODO can it statically generate pages that are created at runtime? E.g. if I create a new blog post, will it automatically upload a static page? It seems that yes, and that this is exactly what Incremental Static Regeneration means:However, Ciro can't find any mention of how to specify where the pages are uploaded to... this is pat of the non-Vercel deployment problem.
- github.com/vercel/next.js/discussions/25410
- vercel.com/docs/next.js/incremental-static-regeneration
- github.com/vercel/next.js/discussions/17711
- www.reddit.com/r/nextjs/comments/mvvhym/a_complete_guide_to_incremental_static/
- github.com/vercel/next.js/discussions/11552#discussioncomment-115595
- stackoverflow.com/questions/62105756/how-to-use-aws-with-next-js
- github.com/vercel/next.js/discussions/17080
- github.com/vercel/next.js/discussions/16852
Can't ISR prerenter by URL query parameters:
That plus the requirement to have one page per file under
pages/ leads to a lot of useless duplication, because then you are forced to place the URL parameters on the pathnames."Module not found: Can't resolve 'fs'" Hell. The main reason this happens seems to be the that in a higher order component, webpack can't determine if callbacks use the require or not to remove it from frontend code. Fully investigated and solved at:
Overviews:
- www.reddit.com/r/reactjs/comments/8evy5d/what_are_the_downsides_to_nextjs/ 2017 What are the downsides to Next.js?
Uncensored Onion service search engine Updated 2025-07-16
This is where "fun" stuff is likely to be.
Great doubt Updated 2025-07-16
Some notable references:
- kotobank.jp/word/大疑-556655 quotes passing referenes by
- en.wikipedia.org/wiki/Hakuin_Ekaku#Taigi_%E2%80%93_great_doubt mentions Hakuin Ekaku's take
Human mitochondrion Updated 2025-07-16
DNA stuff at: human mtDNA.
Market liquidity Updated 2025-07-16
Nest.js Updated 2025-07-16
Arch Linux Updated 2025-07-16
Respect. Big respect. Those people are hardcore from scratch hackers, and their wiki is amazing: wiki.archlinux.org/
But can you build the ISO full from source: Linux distribution buildable from source
FeathersJS Updated 2025-07-16
Looks interesting.
It seems to abstract the part about the client messaging the backend, which focuses on being able to easily plug in a number of Front-end web framework to manage client state.
Uses Socket.IO, which allows the client Javascript to register callbacks when data is updated to achieve Socket.IO, e.g. their default chat app does:so that message appear immediately as they are sent.
client.service('messages').on('created', addMessage);Their standard template from But it is in itself a completely boring app with a single splash page, and no database interaction, so not a good showcase. The actual showcase app is feathersjs/feathers-chat.
feathers generate app on @feathersjs/cli@4.5.0 includes:which looks promising! They don't have a default template for a Front-end web framework however unfortunately: docs.feathersjs.com/guides/frameworks.html#the-feathers-chat lists a few chat app versions, which is their hello world:
- Front-end web framework: not built-in on generator, but there are some sample repos pointed from the documentation, and they did work out-of-box:
And there is no official example of the chat app that is immediately deployable to Heroku: FeathersJS Heroku deployment, all setups require thinking.
JavaScript memory usage benchmark Updated 2025-07-16
In this section we will use the file nodejs/bench_mem.js, tests are run on Node.js v16.14.2 from NVM, Ubuntu 21.10, on Lenovo ThinkPad P51 (2017) which has 32 GB RAM.
Related answer: stackoverflow.com/questions/12023359/what-do-the-return-values-of-node-js-process-memoryusage-stand-for/72043884#72043884
First using
topp from stackoverflow.com/questions/1221555/retrieve-cpu-usage-and-memory-usage-of-a-single-process-on-linux/40576129#40576129 let's observe the memory usage of some baseline cases.For a Node.js infinite loop nodejs/infinite_loop.jsThis gives approximately:
topp infinite_loop.js- RSS: 20 MB
- VSZ: 230 MB
Adding a single hello world to it as in nodejs/infinite_hello.js and running:leads to:We understand that Node.js preallocates VSZ wildly. No big deal, but it does mean that VSZ is a useless measure for Node.js.
topp infinite_hello.js- RSS: 26 MB
- VSZ: 580 MB
Forcing garbage collection as in nodejs/infinite_hello.js brings it down to 20 MB however:
topp node --expose-gc infinite_hello_gc.jsFinally let's see a baseline for which gives initially:but after a few seconds randomly jumps to:so we understand that
process.memoryUsage nodejs/infinite_memoryusage.js:node --expose-gc infinite_memoryusage.js{
rss: 23851008,
heapTotal: 6987776,
heapUsed: 3674696,
external: 285296,
arrayBuffers: 10422
}{
rss: 26005504,
heapTotal: 9084928,
heapUsed: 3761240,
external: 285296,
arrayBuffers: 10422
}First a baseline case with an array of length 1:This gives the same results as with:
node --expose-gc bench_mem.js n 1node --expose-gc infinite_memoryusage.js. The same result is obtained by doing:a = undefinednode --expose-gc bench_mem.js deallocIf we use we see that the memory is now, unsurprisingly, accounted for under Results for different N:We see therefore that typed arrays are much closer to what they advertise (4 bytes per element), even for smaller element counts, as expected.
Int32Array typed array buffers instead of a simple Array:node --expose-gc bench_mem.js array-buffer n NarrayBuffers, e.g. for N 1 million:{
rss: 31776768,
heapTotal: 6463488,
heapUsed: 3674520,
external: 4285296,
arrayBuffers: 4010422
}|| N
|| `arrayBuffers`
|| `rss`
|| `rss` per elem
| 1 M
| 4 MB
| 31 MB
| 5
| 10 M
| 40 MB
| 67 MB
| 4.6
| 100 M
| 40 MB
| 427 MB
| 4Now let's try one million objects of type gives:Disaster! Memory usage is up to 70 MB! Why?? We were expecting only about 24, 4 baseline + 2 * 10 for each million int?!
{ a: 1, b: -1 }:node --expose-gc bench_mem.js obj{
rss: 138969088,
heapTotal: 105246720,
heapUsed: 70103896,
external: 285296,
arrayBuffers: 10422
}And now an equivalent version using gives the same result.
class:node --expose-gc bench_mem.js classLet's try Array:is even worse at 78 MB!! OMG why.
node --expose-gc bench_mem.js arr{
rss: 164597760,
heapTotal: 129363968,
heapUsed: 78117008,
external: 285296,
arrayBuffers: 10422
} LK-99 Updated 2025-07-16
Node.js standard library Updated 2025-07-16
Dan Jewett Updated 2025-07-16
MacKenzie Bezos' new husband after she divorced Bezos.
www.dailymail.co.uk/femail/article-9338723/Who-billionaire-Mackenzie-Scotts-new-husband-Dan-Jewett.html Who IS billionaire Mackenzie Scott's new husband Dan Jewett?
Divorce of Jeff and MacKenzie Bezos Updated 2025-07-16
M. genitalium whole cell model by Covert lab Updated 2025-07-16
www.wholecellviz.org/viz.php awesome visualization of simtk, paper: www.ncbi.nlm.nih.gov/pmc/articles/PMC3413483/ A Whole-Cell Computational Model Predicts Phenotype from Genotype - 2013 - Jonathan R. Karr.
Followed up by the E. Coli Whole Cell Model by Covert Lab.
Microcontroller vs CPU Updated 2025-08-08
MicroPython Updated 2025-08-08
It is interpreted. It actually implements a Python (-like ?) interpreter that can run on a microcontroller. See e.g.: Compile MicroPython code for Micro Bit locally.
MLperf v2.1 ResNet Updated 2025-07-16
Instructions at:
Ubuntu 22.10 setup with tiny dummy manually generated ImageNet and run on ONNX:
sudo apt install pybind11-dev
git clone https://github.com/mlcommons/inference
cd inference
git checkout v2.1
virtualenv -p python3 .venv
. .venv/bin/activate
pip install numpy==1.24.2 pycocotools==2.0.6 onnxruntime==1.14.1 opencv-python==4.7.0.72 torch==1.13.1
cd loadgen
CFLAGS="-std=c++14" python setup.py develop
cd -
cd vision/classification_and_detection
python setup.py develop
wget -q https://zenodo.org/record/3157894/files/mobilenet_v1_1.0_224.onnx
export MODEL_DIR="$(pwd)"
export EXTRA_OPS='--time 10 --max-latency 0.2'
tools/make_fake_imagenet.sh
DATA_DIR="$(pwd)/fake_imagenet" ./run_local.sh onnxruntime mobilenet cpu --accuracyLast line of output on P51, which appears to contain the benchmark resultswhere presumably
TestScenario.SingleStream qps=58.85, mean=0.0138, time=0.136, acc=62.500%, queries=8, tiles=50.0:0.0129,80.0:0.0137,90.0:0.0155,95.0:0.0171,99.0:0.0184,99.9:0.0187qps means queries per second, and is the main results we are interested in, the more the better.Running:produces a tiny ImageNet subset with 8 images under
tools/make_fake_imagenet.shfake_imagenet/.fake_imagenet/val_map.txt contains:val/800px-Porsche_991_silver_IAA.jpg 817
val/512px-Cacatua_moluccensis_-Cincinnati_Zoo-8a.jpg 89
val/800px-Sardinian_Warbler.jpg 13
val/800px-7weeks_old.JPG 207
val/800px-20180630_Tesla_Model_S_70D_2015_midnight_blue_left_front.jpg 817
val/800px-Welsh_Springer_Spaniel.jpg 156
val/800px-Jammlich_crop.jpg 233
val/782px-Pumiforme.JPG 285TODO prepare and test on the actual ImageNet validation set, README says:
Prepare the imagenet dataset to come.
Since that one is undocumented, let's try the COCO dataset instead, which uses COCO 2017 and is also a bit smaller. Note that his is not part of MLperf anymore since v2.1, only ImageNet and open images are used. But still:
wget https://zenodo.org/record/4735652/files/ssd_mobilenet_v1_coco_2018_01_28.onnx
DATA_DIR_BASE=/mnt/data/coco
export DATA_DIR="${DATADIR_BASE}/val2017-300"
mkdir -p "$DATA_DIR_BASE"
cd "$DATA_DIR_BASE"
wget http://images.cocodataset.org/zips/val2017.zip
wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
unzip val2017.zip
unzip annotations_trainval2017.zip
mv annotations val2017
cd -
cd "$(git-toplevel)"
python tools/upscale_coco/upscale_coco.py --inputs "$DATA_DIR_BASE" --outputs "$DATA_DIR" --size 300 300 --format png
cd -Now:fails immediately with:The more plausible looking:first takes a while to preprocess something most likely, which it does only one, and then fails:
./run_local.sh onnxruntime mobilenet cpu --accuracyNo such file or directory: '/path/to/coco/val2017-300/val_map.txt./run_local.sh onnxruntime mobilenet cpu --accuracy --dataset coco-300Traceback (most recent call last):
File "/home/ciro/git/inference/vision/classification_and_detection/python/main.py", line 596, in <module>
main()
File "/home/ciro/git/inference/vision/classification_and_detection/python/main.py", line 468, in main
ds = wanted_dataset(data_path=args.dataset_path,
File "/home/ciro/git/inference/vision/classification_and_detection/python/coco.py", line 115, in __init__
self.label_list = np.array(self.label_list)
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (5000, 2) + inhomogeneous part.TODO!
M. mycoides JCVI strain Updated 2025-07-16
www.newyorker.com/magazine/2022/03/07/a-journey-to-the-center-of-our-cells A Journey to the Center of Our Cells (2022) by James Somers comments on M. genitalium in general, and in particular on the JCVI strains.
SuperTuxKart Updated 2025-07-16
It is a shame, but this game just doesn't feel good. The controls are just not as snappy as Mario Kart 64, the levels are too wide which limits player interaction, and the weapons feel clumsy weak and unexciting. These are all aspects that the closed source smashkarts.io gets pretty well.
Ardour (software) Updated 2025-07-16
Weight: heavy.
MIDI support is kind of secondary: www.youtube.com/watch?v=vnkJ0uYXMG8, e.g. how to export MIDI? discourse.ardour.org/t/export-an-entire-project-into-a-midi-file/88116
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