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42,067,738 | mitchbob | 2024-11-06T19:17:10 | Warren Washington, Groundbreaking Climate Scientist, Dies at 88 | null | https://www.nytimes.com/2024/11/06/climate/warren-washington-dead.html | 5 | 3 | [
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42,067,756 | nutellalover | 2024-11-06T19:18:25 | I built an AI hitch to have better dates | null | https://www.mihaileric.com/posts/i-built-an-ai-hitch/ | 2 | 0 | [
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42,067,771 | brisky | 2024-11-06T19:19:41 | Did – Decentralized global social feed | null | https://github.com/did-1 | 4 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,067,784 | ioblomov | 2024-11-06T19:20:56 | The Trump Trades Worked | null | https://www.bloomberg.com/opinion/articles/2024-11-06/the-trump-trades-worked | 3 | 1 | [
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42,067,833 | mariuz | 2024-11-06T19:24:20 | We Built the BFCM 2023 Globe | null | https://shopify.engineering/how-we-built-shopifys-bfcm-2023-globe | 2 | 0 | null | null | null | no_error | How We Built the BFCM 2023 Globe (2024) - Shopify | null | Diego Macario Bello | Every year for Black Friday Cyber Monday (BFCM), we put together a real-time visualization of purchases made through Shopify-powered merchants worldwide. This year we're cooking up something big, and in anticipation we wanted to show you how we built the globe last year.
Video of BFCM 2023 Globe
Besides going for better visuals, a big focus was performance. We've put together an interactive deep dive into how we built and optimized the 3D visuals using technologies such as Three.js and React-three-fiber.
Before we jump in, here's the globe with simulated data that you can play with.
Arcs
The arcs flying around are the most important component. Each one represents an order being placed in real time, and they travel from the merchant to the buyer's location.
These can be defined as a Bézier curve with 4 control points. Each point is a 3-dimensional vector.
P0: located at start position
P1: located 25% of the way between start and end
P2: located 75% of the way between start and end
P3: located at end position
Arc height can be adjusted by moving control points P1 and P2 away from the surface.
To render the arc, we create a mesh that is a strip of triangles moving along the curve. This gives us the ability to control thickness and have better flexibility for stylizing it with shaders.
You'll notice however that as you move the mesh around, it disappears from certain angles. We needed a way to make it always face the viewer. The solution was surprisingly simple.
The red lines along the curve represent the tangent at those points. Taking the cross product of the tangent with the direction of the camera gives a new vector. This is used in the vertex shader to offset the position so the mesh always faces the viewer.
To texture the arcs, the UVs of the mesh are defined with v going from 0 at the start to 1 at the end. So a vertex in the middle of the arc would have v being 0.5.
Here it is represented with color = uv.y
To animate an arc, the current age of it has to be calculated. This is given by:
The age is normalized to the range 0 to 2.
age = 0: the arc is starting
age = 1: the arc has reached its destination
age = 2: the entire arc trail animation has fully played out
To achieve the dissolve type effect we wanted, we use the UV and the age of the arc and pass that into a noise function.
As new orders come in, these arc meshes are created and added to the globe. They each have their own startTime attribute. As the uTime value increases each frame, all arcs animate independently.
This was the initial setup we had, and was based on code we had been using for many years. There are many implementations of arc rendering floating around the web, and it's very common for each arc to have its own mesh.
But it's not very performant.
In previous years we were limited by how many arcs we could draw per frame. Rendering over 1000 arcs at once would bring some mobile devices to a crawl. We finally found our solution: instancing.
Optimizing the arcs
Instancing is a technique where multiple copies (instances) of the same base mesh can be drawn at the same time on the GPU. Each instance can have its own unique properties such as position or rotation, but they all share the same geometry and material data. This results in a single draw call. The shared geometry also means that only the vertices of the base mesh need to be uploaded to the GPU.
Without instancing, each mesh requires a separate draw call, as the CPU must send individual data sets to the GPU for each mesh. This increases memory usage and impacts performance.
React-three-fiber and Three.js support instancing out of the box.
But how can a common piece of geometry be used to represent every shape of arc instance? For this we landed on an approach to deform the geometry in the vertex shader.
Instead of computing the Bezier curve for each mesh on the CPU, it was all done on the GPU. Even though instances share the same geometry data, they can have their own unique attributes. These are known as instanced buffer attributes.
For each arc we need the P0, P1, P2, P3 control points. There are 3 floats for each of these to represent the x, y, and z coordinates. We also need a single float for the start time.
The data for each arc is then interleaved into a single large buffer.
Data layout of an interleaved buffer
Inside the vertex shader we can then use these attributes to recreate the curve. Since each vertex has a UV attribute between 0 and 1 depending on where it is along the curve, this can be used to calculate its position.
As new orders come in, we don't have to upload new geometry anymore. This is our new flow:
Loop through data until an arc is found with age > 2. This means the arc is finished
Calculate new control points & start time based on the order's origin and destination
Upload the data to the GPU only for that specific arc.
With this new method we are able to render tens of thousands of arcs in a single draw call. Our initial benchmarking found we can render up to a million arcs at once on an M1 laptop. This should give us a good deal of elbow room for years to come!
City Dots
The glowing dots on the globe represent cities with placed orders in the past 24 hours. We debated whether to use meshes aligned to the surface or to use particles that always face the camera.
The difference is subtle, but we chose instanced particles with gl.POINTS since they lead to a nicer glow on the horizon.
See for yourself with the slider below. The left side is surface aligned quads, and the right side are the billboarded points.
Fireworks
We really wanted to find a way to celebrate merchants having their first sale. What better way than to have fireworks go off above their city!
Similar to our arcs, we wanted to see if all fireworks could be done in a single draw call. We generate the base mesh by using Three.js's IcosahedronGeometry and connect triangle strips from the center to each vertex.
To give the mesh the look of a firework burst, we gradually lower vertices along each trail to simulate gravity.
The UVs on each trail are treated the same way as the arcs with v starting at 0 at the start and going to 1 towards the end. This allows us to easily animate the trails bursting outwards.
We also use the noise effect from the arcs to simulate the burst dissipating over time. This is combined with a delayFactor so that the burst trails have a bit of time before starting to disappear.
The whole effect really comes together once bloom is added. This globe was the first where we added postprocessing effects and it added the extra visual oomph we were looking for.
While the bursts themselves were really satisfying to look at, they were missing a launch trail coming up from the ground. We didn't want to introduce a second mesh for this, so instead we have an additional long triangle strip that starts at the origin and goes to the center of the burst.
We added a geometry attribute called isBurstTrail which is false for any vertex part of that launch trail, and true for the burst trails. This lets us drive the animation for each part.
Linearly animating t from 0 to 1 felt a bit too slow so we experimented with different easing values. Cubic easing gives it the speed we were looking for.
We also added some shader uniforms for launch trail height, burst size, and rotation offsets. This let us group together fireworks in fun ways.
The best part is this is all still one single draw call. Each separate firework has its own start time attribute similar to the arcs so they can animate in and out independently.
The end result is endlessly fascinating to watch.
Camera Animations
One of the most delightful features of this globe was the way the camera moved. You could search the name of any city, and no matter where the camera was at that moment, it would move along a beautiful path until it framed the city in the horizon.
The simplest way to go about this is with spherical interpolation:
Convert the 3D start position of the camera into spherical coordinates.
Convert the 3D end position of the camera into spherical coordinates.
Interpolate between the start and end points over time.
The spherical coordinates have radius, phi, and theta attributes which relate to zoom, latitude, and longitude.
In order to make sure the camera looks at the desired city as it moves, the lookAt function on the camera can be used.
Ideally we want the camera tilted down so that it frames the city with the horizon. Offsetting the phi angle slightly downwards achieves the desired effect.
There is a hidden problem here though. If you travel to New York or London everything works great, but watch what happens when you travel to Sydney.
Why does the world flip upside down when we visit Sydney?
The lookAt function uses the up property of the camera to establish which way is up.
The up property is a reference direction, not the actual upward direction of the camera. Even when the camera rotates, the up property stays as [0, 1, 0] unless we manually change it.
When the camera moves to Sydney, the angle between the up property and the actual upward direction of the camera becomes greater than 90 degrees. This causes the camera to flip.
There's luckily a simple fix for that. We need to set the up property each frame, and we can use the phi and theta values to calculate it.
With that fix in place, we now have a system that works perfectly for any city on earth. Try traveling to Sydney to see for yourself:
Animated Pins
This globe's main addition was showing insights about individual cities. To promote discovery, small pins pop out of the globe that you could tap to navigate to that city.
To give the pins a satisfying entrance animation, we turned to react-spring.
Here's a comparison of an early pin animation (left) with the final one (right) that shows how a bit of easing can go a long way to giving a polished feel.
Airplanes
Did you notice the tiny airplanes flying around the globe? They are one of many little surprises to discover. They each follow orbits that pass through two random cities.
Here's a quick way to achieve the circular motion:
Parent the airplane to an empty object (this will be the pivot point)
Offset the airplane by a given amount
Rotate the pivot object each frame
The airplane is now orbiting around the equator. Aligning its orbit to pass through two cities turned out to be simpler than we thought:
This method works perfectly fine and is suitable for the small amount of airplanes we have. However, we really wanted to see if we could animate all the airplanes in the GPU in a single draw call. For this to work, we had to replicate the above in a shader.
We start by creating the instanced mesh.
In this case we can't use separate pivot objects, but we can achieve the same effect with trigonometry in the shader. The airplane material is a MeshBasicMaterial, and we can use the onBeforeCompile method to modify its vertex shader.
This gives the same effect as the airplanes pivoting around the origin. Since each instance has its own transformation matrix, we can set it to a matrix representing the lookAt rotation from before.
The airplanes are now rendered in a single draw call on the GPU. Animating the airplanes is just a matter of updating the currentTime uniform each frame.
While we could have rendered tens of thousands of airplanes without breaking a sweat, we settled on a much more reasonable dozen.
The only airplane on the globe that isn't instanced is a special "Shopify Airplane" that flies across different cities. For this we use 6 orbits and interpolate between them. To make it look natural, we make the airplane bank when it transitions from one orbit to the next. It also sways horizontally and vertically by following some sinusoidal waves.
At the height of BFCM, with thousands of orders flying around the globe every second, hopping on the Shopify Airplane was truly epic.
Loopy Arcs
Loopy arcs were the source of a lot of jokes such as "no wonder my order took 6 weeks to ship, apparently it was doing backflips in the stratosphere."
Having an order do a loop in outer space is certainly not the most efficient way of getting it from point A to point B, but they sure add a nice whimsical feel to our globe.
In order to have as much control over the animations as possible, we opted not to use Bézier splines. Instead we wanted to try defining the animations using keyframes and cubic Hermite splines. These are common for 3D animations, and give an artist more fine grained controls over the look and feel.
Let's take a look.
Above is a box that slides back and forth along the X axis. On the right is a Hermite curve made up of two separate splines (the left half and the right half).
Each spline is defined by a start and end point, as well as a slope (or tangent) for each point. You can think of each point/slope pair as a keyframe. The X axis represents time, and the Y axis represents the value.
This is what the three keyframes above look like in code:
So how do we get the value of the curve for an arbitrary time? For example, 3 seconds.
Find the keyframe that comes before 3 seconds, and the keyframe that comes after. For a Hermite curve with only a few keyframes we can do a linear search. For one with many more keyframes we could do a binary search. There are also more sophisticated techniques that make finding the right keyframes a constant lookup operation, but for our case we can just do a simple linear search. For time = 3 seconds, we find keyframe[1] and keyframe[2].
Interpolate between both keyframes Now that we have both keyframes, we can interpolate between them using the following function:
In order to animate more than one property, we just need to create a separate curve for each property. In the example below we move the box in an arc-like motion. The red curve represents the X position of the box, while the green one represents its Y position.
Here are the curves for moving the box in a circle.
In order to get our loopy arcs, we had to combine the arc-like motion with the circular motion above. The final curves end up looking like this:
Now that we have a beautiful loopy arc, the question becomes how do we orient our coordinate system above so that the arc flies between two cities?
Our friend lookAt comes in handy once again.
This time we use the Matrix4 version of lookAt because it lets us specify the up axis. This allows us to create a rotation matrix that rotates our coordinate system so that the X axis lines up between both cities, and the Y axis comes out of the globe at the desired spot.
If we want to find the position on our animation curve relative to this new coordinate system, we can apply our lookAtMatrix.
Now comes the real challenging part. How do we instance this?
The first thing to note is that the geometry of the loopy arcs is identical to the geometry of regular arcs. The way the triangles are arranged and billboarded, and the way the UVs are used to fade out the arcs with noise are all the same.
What's different about loopy arcs are the attributes we upload to the GPU.
To describe a loopy arc we need to upload all the keyframes of our animation curves and some additional data to construct a transformation equivalent to the lookAtMatrix.
Each keyframe is 4 floats of data. Once we added up all the floats we needed to pass to the GPU, we were at 94. That's 376 bytes of data per arc, which turns out isn't possible to upload. WebGL displayed this error when we tried:
Stride is over the maximum stride allowed by WebGL
The maximum stride allowed by WebGL is 255 bytes.
We started to reconsider our options. We thought about storing the data in a texture, which is a technique called Vertex Texture Animation (VAT). This would get around the limits, but would add some complexity. Luckily we ended up noticing that a lot of the floats we were trying to upload were equal to zero. You can see this yourself in the graphs above. All the flat slopes are equal to zero, and there are a lot of them. Additionally, a lot of the floats that are nonzero are equal to each other or equal but negated.
After identifying which of the attributes were unique, we ended up only needing to upload 17 floats per arc. WebGL could handle that without any problems as it was way below the 255 byte limit. The rest of the values were hardcoded or computed directly in the shader.
With that we had beautiful instanced loopy arcs!
In retrospect, there are definitely simpler ways we could have implemented these. A combination of Bezier curves and circle equations would have been much simpler and more performant. However we were curious about experimenting with Hermite curves to allow for more bespoke animations. We'll explore this more for a future Globe!
Globe Material
In the early stages of the project, we experimented with a wide range of styles for the globe. This idea kept coming back of a hand-painted aesthetic.
We toyed with effects like toon-shading and ink-shading, but eventually we found ourselves drawn to a style that mirrored the look of a risograph print.
Depiction of Risograph style artwork used as inspiration for Globe.
Implementing noise
A key element of risograph prints is the noise that you see all over them. They are covered with little dots of varying sizes. We initially tried to implement that as a screen space effect using the react-postprocessing library, but it never felt quite right.
We needed to add noise to the water, land and atmosphere materials of our globe. The challenge was how to apply noise to the surface of a sphere with no visible seams and without it stretching or getting distorted at the poles.
We tried a few different 2D and 3D noise functions like Perlin noise, Simplex noise and Worley noise (also called Voronoi noise or cellular noise). It wasn't until we discovered psrdnoise, and in particular the flow noise one can create with psrdnoise, that we felt we had found what we were looking for.
Psrdnoise is a variant of Simplex noise that tiles in 2D and 3D, and that supports an animation technique called flow noise in 3D. It's fairly new and was published in 2022.
3D psrdnoise can be applied to a sphere without any modifications and it gives noise with no seams, stretching or distortion. The following example walks through how we apply 3D psrdnoise to a sphere. The number in the bottom right corresponds to the explanation below.
Start with a standard THREE.SphereGeometry. The color gradient represents the vertex positions visualized as colors.
Feed the vertex positions into the noise function. It returns a value of 0 to 1 for every point on the sphere. Use a time value to animate it.
Change the scale by multiplying vertex positions by a scaling factor. As the scaling factor gets bigger, the noise gets smaller. With 3D noise there are no seams, stretching or distortion.
Add another layer of the psrdnoise function again using a "fractal sum" technique. This creates flow noise.
Add two more layers of the psrdnoise function to make the flow noise more detailed.
Scale the noise.
3D psrdnoise is wonderful, but it's not without its downsides. It is computationally expensive, especially when calling the psrdnoise function more than once per fragment to create flow noise. Because of that we switched to using the cheaper 2D psrdnoise function. However, now we had to fix issues with seams, stretching and distortion:
Start with an icosphere. Use the UVs of the icosphere as inputs to the 2D psrdnoise function. Notice the seam.
Scale the noise. The seam is a little less noticeable, but it's still there. There is a lot of stretching at the equator and distortion at the poles.
The stretching and distortion is caused by the default UV mapping of the icosphere.
Switch to a custom UV layout consisting of six round planes. There is no more stretching or distortion.
Seams are still present, but they will go away after the next steps.
Create flow noise by calling the 2D psrdnoise function one additional time.
Add two additional psrdnoise calls.
Scale the noise. The seams have now disappeared.
Below is a comparison of 2D psrdnoise (left) and 3D psrdnoise (right). While the 3D noise does have some subtle visual improvements, we opted to compromise for the more performant version.
Next time you need to use a noise function, give psrdnoise a shot!
Material optimizations
The standard shader in Three.js is a PBR (Physically Based Rendering) shader. It's great for realism, but it's also more computationally expensive, especially on mobile devices.
To simplify the shader, we use the onBeforeCompile method from before to strip down to only the most necessary parts. It was mostly done through trial and error of removing bits that didn't have any effect on the appearance.
The final shader was around a hundred lines vs the thousands in the standard shader, and we switched most to be vertex based instead of fragment based.
Can you tell the difference? The left globe is the PBR one.
Stars
The stars in the distance ended up using psrdnoise as well. Creating a really nice starry sky shader only ends up taking a few lines of code.
Start with an icosphere with the same UVs as our globe.
Use the UVs of the sphere as inputs for the 2D psrdnoise function. The noise is not animated because a time value is not given as an input.
Scale and stretch the UVs.
Feed the noise to the pow function. Increasing the exponent filters the noise so that only the brightest parts remain.
Multiply the color by an intensity factor to add more bloom.
Here is the corresponding GLSL code:
Wrapping up
Building the 2023 BFCM Globe was a journey in pushing the boundaries of what is possible with instancing and WebGL. We look forward to sharing what we've been cooking up for this year's Globe!
| 2024-11-07T13:26:46 | en | train |
42,067,848 | ulrischa | 2024-11-06T19:25:40 | Advancements for Black-Footed Ferret Conservation Continue with Cloned Ferret | null | https://www.fws.gov/press-release/2024-11/advancements-black-footed-ferret-conservation-continue-new-offspring-cloned | 2 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,067,864 | nfriedly | 2024-11-06T19:26:36 | What if everyone pointed a laser at the moon? [xkcd's What If?] [video] | null | https://www.youtube.com/watch?v=JqFSGkFPipM | 2 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,067,902 | ilius2 | 2024-11-06T19:29:10 | Clip leads made with fake wire [video] | null | https://www.youtube.com/watch?v=15sMogK3vTI | 4 | 1 | [
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42,067,905 | handfuloflight | 2024-11-06T19:29:16 | Localai | null | https://localai.io/ | 3 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,067,906 | paulpauper | 2024-11-06T19:29:17 | Does History Have an "End"? | null | https://www.secondbest.ca/p/does-history-have-an-end | 2 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,067,918 | paulpauper | 2024-11-06T19:29:56 | The truth about latchkey kids | null | https://nevermindgenx.substack.com/p/the-truth-about-latchkey-kids | 27 | 24 | [
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42,067,949 | sweca | 2024-11-06T19:31:59 | Easy and straightforward SRP PAKE library in TypeScript | null | https://github.com/httpjamesm/secure-remote-password-js | 2 | 1 | [
42067950
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42,067,973 | rntn | 2024-11-06T19:33:49 | Japan's declining births on track to fall below 700k | null | https://www.asahi.com/ajw/articles/15497062 | 42 | 62 | [
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42,067,985 | josephcsible | 2024-11-06T19:34:28 | The first 240W USB-PD charger you can buy | null | https://www.theverge.com/2024/11/6/24289498/delta-electronics-240w-usb-c-pd-charger-first-adapter | 5 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,067,993 | cardo-podcast | 2024-11-06T19:35:03 | null | null | null | 1 | null | [
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42,068,020 | gsarjeant | 2024-11-06T19:36:34 | Google Zanzibar Isn't Flexible | null | https://www.osohq.com/post/google-zanzibar-isnt-flexible | 5 | 1 | [
42068021
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42,068,026 | nanovision | 2024-11-06T19:36:46 | From WordPress to Truth Social: Donald Trump's Social Media Journey | null | https://mattisnotwp.com/from-wordpress-to-truth-social-donald-trumps-social-media-journey/ | 2 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,068,116 | CharlieEarl | 2024-11-06T19:42:21 | null | null | null | 1 | null | [
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42,068,144 | mfiguiere | 2024-11-06T19:43:28 | Intel sued over Raptor Lake voltage instability | null | https://www.theregister.com/2024/11/06/intel_sued_over_raptor_lake_chips/ | 5 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,068,193 | shry4ns | 2024-11-06T19:45:50 | Chat.com | null | https://www.chat.com | 3 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,068,197 | e2e4 | 2024-11-06T19:46:02 | Vercel Makes Changes to Next.js to Simplify Self-Hosting | null | https://thenewstack.io/vercel-makes-changes-to-next-js-to-simplify-self-hosting/ | 3 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,068,198 | marcelsalathe | 2024-11-06T19:46:09 | null | null | null | 1 | null | null | null | true | null | null | null | null | null | null | null | train |
42,068,225 | tech_voyager | 2024-11-06T19:47:44 | Ask HN: What SaaS metrics turned out to be completely useless? | null | null | 12 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,068,269 | smooke | 2024-11-06T19:50:55 | null | null | null | 1 | null | null | null | true | null | null | null | null | null | null | null | train |
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42,068,311 | bookofjoe | 2024-11-06T19:53:29 | Blood test could help diagnose bipolar disorder – some researchers are sceptical | null | https://www.nature.com/articles/d41586-024-03616-7 | 2 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,068,324 | automationist | 2024-11-06T19:54:06 | Ask HN: How to sell a domain (GPT.chat) to OpenAI? | OpenAI bought ChatGPT.com and Chat.com, and I was getting tens of thousands of visitors from GPT.chat bc a lot of people search "gpt chat" instead of "chat gpt." Would they spare money to buy this domain? | null | 2 | 1 | [
42069357
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42,068,415 | jorzel | 2024-11-06T20:00:06 | Why Bad Code Exists | null | https://www.industriallogic.com/blog/why-bad-code-exists/ | 5 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,068,418 | probermike | 2024-11-06T20:00:20 | Send Fax from iPhone | null | https://fax-flow.com/ | 4 | 0 | [
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42,068,454 | petrusnonius | 2024-11-06T20:02:43 | Ask HN: Are there VC funds that focus on profitability with minimal rounds? | I’m exploring venture capital funds that prioritise sustainability and profitability over continuous fundraising. Specifically, I’m interested in funds that provide an initial round of capital, aiming for the business to achieve profitability—an approach sometimes referred to as "seedstrapping". Ideally, these funds would be open to B2C investments in Europe.<p>I’ve identified a few such funds:<p>• OpenSky Ventures: An early-stage VC firm investing in category-disrupting companies, led by experienced entrepreneurs with a track record of successful exits.[0]<p>• D2 Fund: Focuses on funding capital-efficient B2B software businesses in the UK and Europe, emphasizing efficient entrepreneurship and mission-critical products.[1]<p>• TinySeed: A remote accelerator designed for early-stage SaaS companies, offering funding and mentorship to help founders grow sustainably without the pressure of traditional VC expectations.[2]<p>• Indie.vc: Known for its unconventional approach, funding startups with a focus on profitability and sustainability.[3]<p>For context, Anu Atluru’s essay, “One-Round Wonder”[4], explores the concept of startups achieving success with a single funding round.<p>Are there other funds or investment models that align with this “seedstrapping” philosophy? Insights or experiences with such funds would be greatly appreciated.<p>[0]opensky.vc<p>[1]https://www.d2.fund/<p>[2]tinyseed.com<p>[3]businessofbusiness.com<p>[4]https://www.workingtheorys.com/p/one-round-wonder | null | 4 | 1 | [
42069153
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42,068,456 | null | 2024-11-06T20:02:56 | null | null | null | null | null | null | [
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42,068,461 | null | 2024-11-06T20:03:27 | null | null | null | null | null | null | [
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42,068,465 | ejboustany | 2024-11-06T20:03:39 | null | null | null | 1 | null | [
42068466
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42,068,472 | elashri | 2024-11-06T20:03:57 | Notice of breaking changes for GitHub Actions | null | https://github.blog/changelog/2024-11-05-notice-of-breaking-changes-for-github-actions/ | 4 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,068,485 | squircle | 2024-11-06T20:04:41 | Caring for yourself while caring for others | null | https://magazine.medlineplus.gov/article/caring-for-yourself-while-caring-for-others | 220 | 30 | [
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42,068,508 | PaulHoule | 2024-11-06T20:06:23 | Advancing Interpretability in Text Classification Through Prototype Learning | null | https://arxiv.org/abs/2410.17546 | 2 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,068,510 | biorach | 2024-11-06T20:06:29 | Funding restored for man-page maintenance | null | https://lwn.net/Articles/997193/ | 4 | 2 | [
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42,068,542 | danhds | 2024-11-06T20:08:28 | Show HN: The Fake Notepad | null | https://simplenotepad.vercel.app/ | 1 | 3 | [
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42,068,583 | squircle | 2024-11-06T20:11:19 | The Importance of Caring for Each Other (2023) | null | https://sagecollective.org/the-importance-of-caring-for-each-other/ | 1 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,068,587 | laurex | 2024-11-06T20:11:29 | They Searched Through Bands to Solve an Online Mystery | null | https://www.wired.com/story/the-most-mysterious-song-on-the-internet-mystery-solved/ | 3 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,068,619 | div72 | 2024-11-06T20:13:21 | Yes, elections produce stupid results. Is there an alternative? | null | https://demlotteries.substack.com/p/yes-elections-produce-stupid-results | 5 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,068,646 | Anon84 | 2024-11-06T20:15:30 | Understanding Hierarchical Navigable Small Worlds (HNSW) | null | https://www.datastax.com/guides/hierarchical-navigable-small-worlds | 1 | 0 | null | null | null | no_error | Understanding Hierarchical Navigable Small Worlds (HNSW) | null | Phil MiesleDeveloper Advocate | What are Hierarchical Navigable Small Worlds (HNSW)?Introduced in a 2016 paper, Hierarchical Navigable Small World (HNSW) is more than just an acronym in the world of vector searching; it's the algorithm that underpins many vector databases. Available under the Apache Lucene project and other implementations, what makes HNSW stand out from other vector indexing techniques is its approach to tackling a common challenge in data science and artificial intelligence: efficiently finding the nearest neighbors in large multi-dimensional datasets.In a nutshell, HNSW creates a multi-layered graph structure where each layer is a simplified, navigable small world network. A mental analog you may consider is that of a digital map of a road network. Zoomed out, you see how cities and towns are connected by major roads. Zoom in to the city level, and you can see how people within that city connect to each other. At the finest level of zoom, you can see how neighborhoods and communities are interconnected.Similarly, HNSW constructs layers of connections with varying degrees of reachability. This structure allows for remarkably quick and accurate searches, even in vast, high-dimensional data spaces. This post will explore some of the inner workings of HNSW, along with some of its strengths and weaknesses.Why are Hierarchical Navigable Small Worlds Important?Hierarchical Navigable Small Worlds are important as they provide an efficient way to navigate and search through complex, high-dimensional data. This structure allows for faster and more accurate nearest-neighbor searches, essential in various applications like recommendation systems, image recognition, and natural language processing. By organizing data hierarchically and enabling navigable shortcuts, HNSW significantly reduces the time and computational resources needed for these searches, making it a crucial tool in handling large datasets in machine learning and AI.Understanding Similarity SearchSimilarity search is a fundamental technique in data science, used to find data points in a dataset that are 'nearest' to a given query; as these searches are often within a vector space, it is also known as “vector search”. Such searches are used by systems such as voice recognition, image / video retrieval, and recommendation systems (amongst others); more recently it has been brought to the forefront of generative AI as one of the main drivers to improve performance through Retrieval-Augmented Generation (RAG).Traditionally, methods like brute-force search, tree-based structures (e.g., KD-trees), and hashing (e.g., Locality-Sensitive Hashing) were used. However, these approaches do not scale well with large, high-dimensional datasets, leading to the need for more efficient solutions. A graph-based approach called Navigable Small World (NSW) improved on the scaling abilities of its predecessors, but this ultimately has polylogarithmic scaling characteristics, limiting its utility in large-scale applications where there are a large number of both records and dimensions.This is where Hierarchical Navigable Small World (HNSW) enters the frame - it builds on NSW by introducing layers (“hierarchies”) to connect data, much like adding a “zoom out” feature to a digital map.Underpinnings of HNSWBefore going further on HNSW, we should first explore the Navigable Small World (NSW) and a data structure called “skip lists”, both of which make use of a graph-based data structure. In such a structure, the data (nodes) are connected to each other by edges, and one can navigate through the graph by following these edges.Navigable Small Words (NSW)The principle behind Navigable Small Worlds is that every node can be reached within a small number of “hops” from any other node. After determining an entry point (which could be random, heuristic, or some other algorithm), the adjacent nodes are searched to see if any of them are closer than the current node. The search moves to the closest of these neighboring nodes and repeats until there are no closer nodes.In the following illustration, we are searching for the node nearest to “A”, and start by looking at node “0”. That node is connected to three nodes, but the one that is closest to “A” is node “1”. We then repeat the process from node “1”, finding that node “2” is closer than node “1”, and finally that node “3” is closer than node “2”. The nodes connected to “3” are all further away from “A”, so we determine that “3” is the closest node:In building the graph, the M parameter sets out the number of edges a new node will make to its nearest neighbors (the above example uses M=2). A larger M means a more interconnected, denser graph but one that will consume more memory and be slower to insert. As nodes are inserted into the graph, it searches for the M closest nodes and builds bidirectional connections to those nodes. The network nodes will have varying degrees of connectivity, in a similar fashion to how social networks contain users connected to thousands (or millions) of people and others that are connected to hundreds (or tens) of people. An additional Mmax parameter determines the maximum number of edges a node can have, as an excessive number of edges can affect performance.One of the shortcomings of NSW search is that it always takes the shortest apparent path to the “closest node” without considering the broader structure of the graph; This is known as “greedy search”, and can sometimes lead to being trapped in a local optimum or locality – a phenomenon known as “early stopping”. Consider what might happen in the following diagram, where we enter at a different node “0” with a different “A” node:The two nodes connected to node “0” are both further away from “A”, so the algorithm determines that “0” is the closest node. This can happen at any point during the navigation, not just the starting node. It does not try to explore branches beyond any nodes that are further away, as it assumes anything further away is even further away! There are techniques to mitigate this, such as testing multiple entry points, but they add to cost and complexity.Skip ListsThe structure of a skip list allows both insertion and querying operations to be performed on the order of average O(log n) – the time of the operation grows logarithmically with the amount of data, rather than linearly, polylogarithmically, or worse. At the bottom of a skip list, every node is connected to the next node in order. As more layers are added, nodes in the sequence are “skipped”. The following Wikipedia illustration shows how ten nodes might be organized into a four-layer skip list:Skip lists are generally built probabilistically - the likelihood of a node appearing in a layer decreases at higher layers, which means the higher layers have fewer nodes. When searching for a node, the algorithm enters at the “head” of the top layer and proceeds to the next element that is greater than or equal to the target. At this point, the search element is found, or it drops to the next (more fine-grained) layer, and the process repeats until you’ve either found the search node, or you’ve found the adjacent nodes to your search node. Within the context of skip lists and similarity search, “greater than” means “more similar” - in the case of cosine similarity, for example, the closest you can get would be a similarity of 1.Insertion involves determining what layer the node is to enter the structure, seeking to the first node that is “greater than” but keeping track of the last node before the “greater than” node. Once it finds that “greater than” node, it updates the last node to point at the new node, and points the new node to the “greater than” node. It then proceeds to the next layer down, until the node appears in all layers. Deletions follow a similar logic to locating and updating pointers.Introducing Hierarchical Navigable Small Worlds (HNSW)Transitioning from Navigable Small World (NSW) to Hierarchical Navigable Small World (HNSW) marks a shift from polylogarithmic complexity to streamlined logarithmic scaling in vector searches. HNSW marries the swift traversal of skip lists with NSW’s rich interconnectivity, adding a hierarchical twist for enhanced search efficiency.At the heart of HNSW is its tiered architecture, which organizes data into layers of interconnected nodes, allowing rapid skips across unnecessary computations to focus on pertinent search areas. This hierarchical approach, coupled with the small-world concept, optimizes the path to high-precision results, essential for applications demanding quick and accurate retrievals.HNSW not only simplifies the complexity of navigating large datasets but also ensures consistent and reliable performance, underscoring its prowess in modern data-intensive challenges.How HNSW worksLet’s go down a layer and discuss how HNSW works in more detail, including both the building and maintenance of the index, as well as conducting searches within the index.Building the hierarchical structureThe foundation of HNSW lies in its hierarchical structure. Building this multi-layered graph starts with a base layer that represents the entire dataset. As we ascend the hierarchy, each subsequent layer acts as a simplified overview of the one below it, containing fewer nodes and serving as express lanes that allow for longer jumps across the graph.The probabilistic tiering in HNSW, where nodes are distributed across different layers in a manner similar to skip lists, is governed by a parameter typically denoted as mL. This parameter determines the likelihood of a node appearing in successive layers, with the probability decreasing exponentially as the layers ascend. It's this exponential decay in probability that creates a progressively sparser and more navigable structure at higher levels, ensuring the graph remains efficient for search operations.Constructing the navigable graphConstructing the navigable graph in each layer of HNSW is key to shaping the algorithm's effectiveness. Here, the parameters M and Mmax, inherited from the NSW framework, play a crucial role. A new parameter Mmax0 is reserved for the ground layer, allowing for greater connectivity at this foundational level than at the higher levels. HNSW also adds the efConstruction parameter, which determines the size of the dynamic candidate list as new nodes are added. A higher efConstruction value means a more thorough, albeit slower, process of establishing connections. This parameter helps balance connection density, impacting both the search's efficiency and accuracy.An integral part of this phase is the advanced neighbor selection heuristic, a key innovation of HNSW. This heuristic goes beyond simply linking a new node to its nearest neighbors; it also identifies nodes that improve the graph's overall connectivity. This approach is particularly effective in bridging different data clusters, creating essential shortcuts in the graph. This heuristic, as illustrated in the HNSW paper, facilitates connections that a purely proximity-based method might overlook, significantly enhancing the graph's navigability:By strategically balancing connection density and employing intelligent neighbor selection, HNSW constructs a multi-layered graph that is not only efficient for immediate searches but is also optimally structured for future search demands. This attention to detail in the construction phase is what enables HNSW to excel in managing complex, high-dimensional data spaces, making it an invaluable tool for similarity search tasks.Search processSearching in HNSW involves navigating through the hierarchical layers, starting from the topmost layer and descending to the bottom layer, where the target's nearest neighbors are located. The algorithm takes advantage of the structure's gradation – using the higher layers for rapid coarse-grained exploration and finer layers for detailed search – optimizing the path towards the most similar nodes. Heuristics, such as selecting the best entry points and minimizing hops, are employed to speed up the search without compromising the accuracy of the results.If we revisit our simple NSW example from above, we can see how the HNSW search process works. Looking for the nearest neighbor of node “A”, we start by picking a node on the top-most Layer 2 and navigating (as with NSW) to its nearest neighbor node “1”. At this point, we go down a layer to Layer 1, and search to see if this layer has any closer neighbors, which we find as node “2”. We finally go down to the bottom Layer 0, and find that node “3” is closest:Searching within HNSW is similar to searching within NSW, with an added “layer” dimension that allows quick pruning of the graph to only those relevant neighbors.Insertions and DeletionsInsertions and deletions in the Hierarchical Navigable Small World (HNSW) graph ensure its dynamic adaptability and robustness in handling high-dimensional datasets.Insertions:When a new node is added to the HNSW graph, the algorithm first determines its position in the hierarchical structure. This process involves identifying the entry layer for the node based on the probabilistic tiering governed by the parameter mL. The insertion starts from that layer, connecting the new node to the M closest neighbors within that layer. If a neighboring node ends up with more than Mmax connections, the excess connections are removed. The node is then inserted into the next layer down in a similar fashion, and the process continues to the bottom node where Mmax0 is referenced as the maximum connectivity.Deletions:The process of removing a node from an HNSW graph involves careful updating of connections, particularly in its neighbors, across multiple layers of the hierarchy. This is crucial to maintain the structural integrity and navigability of the graph. Different implementations of HNSW may adopt various strategies to ensure that connectivity within each layer is preserved and that no nodes are left orphaned or disconnected from the graph. This approach ensures the continued efficiency and reliability of the graph for nearest-neighbor searches.Both insertions and deletions in HNSW are designed to be efficient while preserving the graph's hierarchical and navigable nature, crucial for the algorithm's fast and accurate nearest-neighbor search capabilities. These processes underscore the adaptability of HNSW, making it a robust choice for dynamic, high-dimensional similarity search applications.Benefits and Limitations of HNSWAs with most any technology, for all the improvements HNSW brings to the world of vector search, it has some weaknesses that may not make it appropriate for all use cases.Benefits of HNSWEfficient Search Performance: HNSW significantly outperforms traditional methods like KD-trees and brute-force search in high-dimensional spaces, as well as NSW, making it ideal for large-scale datasets.Scalability: The algorithm scales well with the size of the dataset, maintaining its efficiency as the dataset grows.Hierarchical Structure: The multi-layered graph structure allows for quick navigation across the dataset, enabling faster searches by skipping irrelevant portions of the data.Robustness: HNSW is known for its robustness in various types of datasets, including those with high dimensionality, which often pose challenges for other algorithms.Limitations of HMemory Usage: The graph structure of HNSW, while efficient for search operations, can be memory-intensive, especially with a large number of connections per node. This imposes a practical limitation on graph size, as operations must be performed in memory if they are to be fast.Complex Implementation: The algorithm's hierarchical and probabilistic nature makes it more complex to implement and optimize compared to simpler structures.Parameter Sensitivity: HNSW performance can be sensitive to its parameters (like the number of connections per node), requiring careful tuning for optimal results.Potential Overhead in Dynamic Environments: While HNSW is efficient in static datasets, the overhead of maintaining the graph structure in dynamic environments (where data points are frequently added or deleted) could be significant.In conclusion, while HNSW offers significant advantages in search efficiency and scalability, especially in high-dimensional spaces, it comes with trade-offs in terms of memory usage and implementation complexity. These factors must be considered when choosing HNSW for specific applications.Try a Vector Database that has all of the benefits of HNSWIn this post, we have explored how HNSW has improved on its predecessors, enabling faster search for large multidimensional datasets. We’ve discussed how the HNSW graphs are built, searched, and maintained, and we have discussed some of the strengths and limitations of HNSW.If you are looking for a vector database that keeps the strengths of HNSW, but eliminates the memory and concurrency problems found in many HNSW implementations, have a look at DataStax Vector Search, which uses the open source JVector project. | 2024-11-07T22:37:15 | en | train |
42,068,653 | redbell | 2024-11-06T20:16:16 | HtmlRAG: HTML Is Better Than Plain Text for RAG Systems | null | https://huggingface.co/papers/2411.02959 | 2 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,068,661 | janandonly | 2024-11-06T20:16:49 | Docling: Docling parses documents and exports them to the desired format | null | https://github.com/DS4SD/docling | 1 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,068,686 | hhs | 2024-11-06T20:18:30 | null | null | null | 3 | null | null | null | true | null | null | null | null | null | null | null | train |
42,068,695 | Traces | 2024-11-06T20:19:10 | null | null | null | 9 | null | [
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42,068,697 | aquastorm | 2024-11-06T20:19:15 | It's 2024. Why Does PostgreSQL Still Dominate? | null | https://www.i-programmer.info/news/84-database/16882-its-2024-why-does-postgresql-still-dominate.html | 1 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,068,710 | mixeden | 2024-11-06T20:20:14 | Dynamic Text-Attributed Graphs Benchmark | null | https://synthical.com/article/DTGB%3A-A-Comprehensive-Benchmark-for-Dynamic-Text-Attributed-Graphs-2f030e56-b9cb-42d3-893e-75ccecd13234 | 2 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,068,757 | laurex | 2024-11-06T20:23:32 | One blood test can identify presence and type of cancer | null | https://health.clevelandclinic.org/the-galleri-test | 1 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,068,759 | breck | 2024-11-06T20:23:38 | null | null | null | 1 | null | null | null | true | null | null | null | null | null | null | null | train |
42,068,773 | ecastroayuso | 2024-11-06T20:24:26 | null | null | null | 1 | null | [
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] | null | true | null | null | null | null | null | null | null | train |
42,068,785 | c420 | 2024-11-06T20:25:13 | Meta asks Supreme Court to dismiss fraud suit over Cambridge Analytica scandal | null | https://www.theguardian.com/technology/2024/nov/06/facebook-cambridge-analytica-lawsuit | 14 | 1 | [
42069699
] | null | null | null | null | null | null | null | null | null | train |
42,068,796 | throwanem | 2024-11-06T20:26:16 | null | null | null | 5 | null | [
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42,068,809 | Mobil1 | 2024-11-06T20:27:19 | null | null | null | 1 | null | null | null | true | null | null | null | null | null | null | null | train |
42,068,814 | testelastic | 2024-11-06T20:27:38 | Jarvis AI | null | https://www.techradar.com/computing/artificial-intelligence/jarvis-ai-is-real-google-accidentally-leaks-its-ai-agent-that-browses-the-web-for-you | 1 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,068,827 | mitchbob | 2024-11-06T20:28:17 | Trump's Victory Is a Major Win for Elon Musk and Big-Money Politics | null | https://www.nytimes.com/2024/11/06/us/elections/trump-musk-america-pac.html | 9 | 5 | [
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42,068,856 | Stamigos | 2024-11-06T20:30:10 | null | null | null | 1 | null | null | null | true | null | null | null | null | null | null | null | train |
42,068,869 | bajialeduchang | 2024-11-06T20:30:43 | null | null | null | 1 | null | [
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42,068,872 | filereaper | 2024-11-06T20:30:52 | Deskflow by Synergy's Symless Team. Wayland Supported Now | null | https://github.com/deskflow/deskflow | 2 | 2 | [
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42,068,923 | geekmetaverse | 2024-11-06T20:35:03 | null | null | null | 1 | null | null | null | true | null | null | null | null | null | null | null | train |
42,068,983 | packetandpine | 2024-11-06T20:39:23 | null | null | null | 1 | null | [
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42,069,001 | bookofjoe | 2024-11-06T20:40:37 | The antibodies don't work! Race to rid labs of molecules that ruin experiments | null | https://www.nature.com/articles/d41586-024-03590-0 | 1 | 0 | null | null | null | no_error | The antibodies don’t work! The race to rid labs of molecules that ruin experiments | null | Kwon, Diana |
Carl Laflamme knew what protein he wanted to study, but not where to find it. It is encoded by a gene called C9ORF72, which is mutated in some people with the devastating neurological condition motor neuron disease, also known as amyotrophic lateral sclerosis. And Laflamme wanted to understand its role in the disease.When he started his postdoctoral fellowship at the Montreal Neurological Institute-Hospital in Canada, Laflamme scoured the literature, searching for information on the protein. The problem was that none of the papers seemed to agree where in the cell this mysterious molecule operates. “There was so much confusion in the field,” Laflamme says.He wondered whether a reagent was to blame, in particular the antibodies that scientists used to measure the amount of the protein and track its position in the cell. So, he and his colleagues decided to test the antibodies that were available. They identified 16 commercial antibodies that were advertised as able to bind to the protein encoded by C9ORF72. When the researchers put them through their paces, only three performed well — meaning that the antibodies bound to the protein of interest without binding to other molecules. But not one published study had used these antibodies. About 15 papers described experiments using an antibody that didn’t even bind the key protein in Laflamme’s testing. And those papers had been collectively cited more than 3,000 times1.Serious errors plague DNA tool that’s a workhorse of biologyLaflamme’s experience isn’t unusual. Scientists have long known that many commercial antibodies don’t work as they should — they often fail to recognize a specific protein or non-selectively bind to several other targets. The result is a waste of time and resources that some say has contributed to a ‘reproducibility crisis’ in the biological sciences, potentially slowing the pace of discovery and drug development.Laflamme is part of a growing community that wants to solve the problem of unreliable antibodies in research. He teamed up with molecular geneticist Aled Edwards at the University of Toronto, Canada, to set up Antibody Characterization through Open Science (YCharOS, pronounced ‘Icarus’), an initiative that aims to characterize commercially available research antibodies for every human protein.There are also efforts under way to produce better-performing antibodies, to make it easier for researchers to find them and to encourage the research community to adopt best practices when it comes to choosing and working with these molecules. Antibody vendors, funding agencies and scientific publishers are all getting in on the action, says Harvinder Virk, a physician–scientist at the University of Leicester, UK. “It’s hard to imagine that a problem that has been going on so long will suddenly change — but I’m hopeful.”Putting antibodies to the testThe immune system produces antibodies in response to foreign substances, such as viruses and bacteria, flagging them for destruction. This makes antibodies useful in laboratory experiments. Scientists co-opt this ability by using them to mark or quantify specific biological molecules, such as a segment of a protein. To be effective, these molecular tags need to have both specificity — a strong affinity for the target — and selectivity — the ability to leave other proteins unmarked.For decades, scientists created these antibodies themselves. They injected proteins into animals, such as rabbits, whose immune systems would generate antibodies against the foreign molecules. To create a longer-term, more consistent supply of antibodies, researchers extracted immune cells from animals and combined them with immortalized cancer cells. When reagent companies began the mass production of antibodies in the 1990s, most researchers shifted to purchasing antibodies from a catalogue. Today, there are around 7.7 million research antibody products on the market, sold by almost 350 antibody suppliers around the world.In the late 2000s, scientists began reporting problems with both the specificity and selectivity of many commercially available antibodies, leading researchers to call for an independent body to certify that the molecules work as advertised. Over the years, a handful of groups have launched efforts to evaluate antibodies.What sets YCharOS apart is the level of cooperation that it has obtained from companies that sell antibodies. When Laflamme and Edwards set out to start YCharOS, they called every single vendor they could find; more than a dozen were interested in collaborating. YCharOS’s industry partners provide the antibodies for testing, free of charge. The partners, along with the funders of the initiative (which include various non-profit organizations and funding agencies), are given the chance to review characterization reports and provide feedback before they are published.YCharOS tests antibodies by comparing their specificity in a cell line that expresses the target protein at normal biological levels against their performance in what’s called a knock-out cell line that lacks the protein (see ‘Ways to validate’).In an analysis published in eLife last year, the YCharOS team used this method to assess 614 commercial antibodies, targeting a total of 65 neuroscience-related proteins2. Two-thirds of them did not work as recommended by manufacturers.“It never fails to amaze me how much of a hit or miss antibodies are,” says Riham Ayoubi, director of operations at YCharOS. “It shows you how important it is to include that negative control in the work.”Antibody manufacturers reassessed more than half of the underperforming antibodies that YCharOS flagged in 2023. They issued updated recommendations for 153 of them and removed 73 from the market. The YCharOS team has now tested more than 1,000 antibodies that are meant to bind to more than 100 human proteins.“There’s still a lot of work ahead,” Laflamme says. He estimates that, of the 1.6 million commercially available antibodies to human proteins, roughly 200,000 are unique (many suppliers sell the same antibodies under different names).“I think the YCharOS initiative can really make a difference,” says Cecilia Williams, a cancer researcher at the KTH Royal Institute of Technology in Stockholm. “But it’s not everything, because researchers will use these antibodies in other protocols, and in other tissues and cells that may express the protein differently,” she says. The context in which antibodies are used can change how they perform.Other characterization efforts are trying to tackle this challenge. Andrea Radtke and her collaborators were part of a cell-mapping consortium called the Human BioMolecular Atlas Program when they set up the Organ Mapping Antibody Panels (OMAPs). OMAPs are collections of community-validated antibodies used in multiplex imaging — a technique that involves visualizing several proteins in a single specimen. Unlike YCharOS, which focuses on conducting rigorous characterizations of antibodies for various applications in one specific context, OMAPs is looking at a single application for the antibodies, but in several contexts, such as in different human tissues and imaging methods. To do so, OMAPs recruits scientists from both academia and industry to conduct validations in their own labs.“Vendors cannot test all possible applications of their antibodies, but as a community we can say ‘let’s try this’,” says Radtke, who now works as a principal scientist at the instrumentation company Leica Microsystems in Bethesda, Maryland. “People are testing things that you would never think you could test.”Expanding the toolboxEven if good antibodies are available, they are not always easy to find. In 2009, Anita Bandrowski, founder and chief executive of the data-sharing platform SciCrunch in San Diego, California, and her colleagues were examining how difficult it was to identify antibodies in journal articles. After sifting through papers in the Journal of Neuroscience, they found that 90% of the antibodies cited lacked a catalogue number (codes used by vendors to label specific products) — making them almost impossible to track down. To replicate an experiment, it’s important to have the right reagents — and proper labelling is crucial to finding them, Bandrowski says.After seeing that a similar problem plagued other journals, Bandrowski and her colleagues decided to create unique, persistent identifiers for antibodies and other scientific resources, such as model organisms, which they called research resource identifiers, or RRIDs. Catalogue numbers can disappear if a company discontinues a product — and because companies create them independently, two different products might end up with the same one. RRIDs solve this.‘A landmark moment’: scientists use AI to design antibodies from scratchIn 2014, Bandrowski and her team started a pilot project3 with 25 journals, in which they asked authors to include RRIDs in their manuscripts. In the years since, more than 1,000 journals have adopted policies that request these identifiers. “We currently have nearly one million citations to RRIDs from papers,” says Bandrowski.Ultimately, the hope is that authors of every journal article will clearly label the resources they used, such as antibodies, with RRIDs, Bandrowski says. “That won’t change reproducibility by itself, but it is the first step.”In addition to being able to track down antibodies, researchers need a way to choose which ones to use. In 2012, Andrew Chalmers, who was then a researcher at the University of Bath, UK, co-founded CiteAb, a search engine to help researchers find the most highly cited antibodies. Over the years, the platform has grown to include more than seven million antibodies — and now also includes, when available, information regarding validations. In May, CiteAb began integrating YCharOS’s characterization data onto its site.“The big challenge is that antibodies are just used in so many different ways, for so many different species that you can’t tick off that an antibody is good or bad,” Chalmers says. Many say that knock-out validation is key, but less than 5% of antibodies on CiteAb have been validated in this way, either by suppliers or through other independent initiatives, such as YCharOS. “There’s a long way to go,” Chalmers says.Stakeholders get involvedLike many others, Virk developed an interest in antibody reliability after a personal experience with bad antibodies. In 2016, Virk received a big grant to study the role of a protein called TRPA1 in airway inflammation. But one of his colleagues mentioned that, on the basis of his own experience, the antibodies he was working with might not be reliable.When Virk put TRPA1 antibodies to the test, he discovered that his colleague was right: of the three most-cited antibodies used to study TRPA1, two didn’t detect the human protein at all, and the other detected several other proteins at the same time. “That was a shock,” Virk says. “At that point, I wanted to leave science — because if things are really this unreliable, what’s the point?”Instead of leaving academia, Virk co-founded the Only Good Antibodies (OGA) community last year, with the aim of bringing together stakeholders — such as researchers, antibody manufacturers, funding agencies and publishers — to tackle the problem of poorly performing antibodies. In February, the OGA community hosted its first workshop, which included individuals from these various groups to discuss how to improve the reproducibility of research conducted with antibodies. They were joined by NC3Rs, a scientific organization and funder, based in London that focuses on reducing the use of animals in research. Better antibodies means fewer animals are used in the process of producing these molecules and conducting experiments with them.Blame it on the antibodiesCurrently, the OGA community is working on a project to help researchers choose the right antibodies for their work and to make it easier for them to identify, use and share data about antibody quality. It is also piloting an YCharOS site at the University of Leicester — the first outside Canada — which will focus on antibodies used in respiratory sciences. The OGA community is also working with funders and publishers to find ways to reward researchers for adopting antibody-related best practices. Examples of such rewards include grants for scientists taking part in antibody-validation initiatives.Manufacturers have also been taking steps to improve antibody performance. In addition to increasingly conducting their own knock-out validations, a number of suppliers are also altering the way some of their products are made.The need to modify antibody-production practices was brought to the fore in 2015, when a group of more than 100 scientists penned a commentary in Nature calling for the community to shift from antibodies generated by immune cells or immune–cancer-cell hybrids, to what are known as recombinant antibodies4. Recombinant antibodies are produced in genetically engineered cells programmed to make a specific antibody. Using these antibodies exclusively, the authors argued, would enable infinite production of antibodies that do not vary from batch to batch — a key problem with the older methods.A few manufacturers are shifting towards making more recombinant antibodies. For example, Abcam, an antibody supplier in Cambridge, UK, has added more than 32,000 of them to their portfolio. “Facilitating the move towards recombinants across life-science research is a key part of improving reproducibility,” says Hannah Cable, the vice-president of new product development at Abcam. “That’s something that antibody suppliers should be doing.”Rob Meijers, director of the antibody platform at the Institute for Protein Innovation in Boston, Massachusetts, a non-profit research organization that makes recombinant antibodies, says that this shift simply makes more business sense. “They’re much more reproducible, you can standardize the process for them, and the user feedback is very positive,” he says.Standardize antibodies used in researchCiteAb’s data have revealed that scientists’ behaviour around antibody use has shifted drastically over the past decade. About 20% of papers from 2023 that involved antibodies used recombinants. “That’s a big change from where we were ten years ago,” says Chalmers, who is now CiteAb’s chief executive.Although the ongoing efforts to improve antibody reliability are a move in the right direction, changing scientists’ behaviour remains one of the biggest challenges, say those leading the charge. There are cases in which researchers don’t want to hear that an antibody they’ve been using for their experiments isn’t actually doing what it’s meant to, Williams says. “If somebody is happy with the result of an antibody, it’s being used regardless, even if it’s certain that it doesn’t bind this protein,” Williams says. Ultimately, she adds, “you can never get around the fact that the researcher will have to do validations”.Still, many scientists are hopeful that recent efforts will lead to much needed change. “I’m optimistic that things are getting better,” Radtke says. “What I’m so encouraged by is the young generation of scientists, who have more of a wolf-pack mentality, and are working together to solve this problem as a community.”
| 2024-11-08T13:41:18 | en | train |
42,069,007 | XzetaU8 | 2024-11-06T20:40:59 | How the Brain Reacts to Movie Scenes | null | https://neurosciencenews.com/brain-movies-networks-27983/ | 2 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,069,012 | greenido | 2024-11-06T20:41:13 | null | null | null | 1 | null | null | null | true | null | null | null | null | null | null | null | train |
42,069,025 | rntn | 2024-11-06T20:41:53 | German firms tested 4-day workweek – here's the outcome | null | https://www.dw.com/en/german-firms-tested-4-day-workweek-heres-the-outcome/a-70685885 | 11 | 1 | [
42070020
] | null | null | null | null | null | null | null | null | null | train |
42,069,108 | yuriynii | 2024-11-06T20:48:05 | Guide: Improving LUGO Moon Wars Collector with Yotey | null | https://yotey.co/articles/guide-improving-lugo-moon-wars-collector-with-yotey.html | 1 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,069,138 | elizabeth01 | 2024-11-06T20:49:43 | null | null | null | 1 | null | null | null | true | null | null | null | null | null | null | null | train |
42,069,147 | todsacerdoti | 2024-11-06T20:50:25 | Exploring Postgres's arena allocator by writing an HTTP server from scratch | null | https://www.enterprisedb.com/blog/exploring-postgress-arena-allocator-writing-http-server-scratch | 2 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,069,160 | MyChannels | 2024-11-06T20:50:50 | null | null | null | 1 | null | null | null | true | null | null | null | null | null | null | null | train |
42,069,242 | impish9208 | 2024-11-06T20:56:38 | FTC Action Against Cash Advance App Dave for Deceiving Consumers | null | https://www.ftc.gov/news-events/news/press-releases/2024/11/ftc-takes-action-against-online-cash-advance-app-dave-deceiving-consumers-charging-undisclosed-fees | 3 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,069,246 | ortusdux | 2024-11-06T20:56:47 | One year, 41M digits: How Luke Durant found the largest known prime number | null | https://www.washingtonpost.com/science/2024/10/23/nvidia-prime-mersenne-gpu-cloud/ | 7 | 6 | [
42071253,
42071445,
42071229,
42071415,
42071240
] | null | null | null | null | null | null | null | null | null | train |
42,069,257 | Geekette | 2024-11-06T20:57:25 | Secrets of a ransomware negotiator | null | https://www.economist.com/1843/2024/07/24/secrets-of-a-ransomware-negotiator | 2 | 1 | [
42069275
] | null | null | null | null | null | null | null | null | null | train |
42,069,259 | squircle | 2024-11-06T20:57:33 | Useless Machine | null | https://en.wikipedia.org/wiki/Useless_machine | 2 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,069,270 | mostech | 2024-11-06T20:58:20 | null | null | null | 1 | null | null | null | true | null | null | null | null | null | null | null | train |
42,069,306 | lqet | 2024-11-06T21:00:10 | null | null | null | 18 | null | [
42069557
] | null | true | null | null | null | null | null | null | null | train |
42,069,316 | ZeljkoS | 2024-11-06T21:00:30 | The Value of Source Code [video] | null | https://www.youtube.com/watch?v=Y6ZHV0RH0fQ | 1 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,069,320 | PaulHoule | 2024-11-06T21:00:44 | Exposure to phthalate compromises brain function in adult vertebrates | null | https://www.sciencedirect.com/science/article/pii/S0147651324012636 | 60 | 13 | [
42071144,
42070418,
42070531,
42070053,
42070938,
42069336,
42071347
] | null | null | null | null | null | null | null | null | null | train |
42,069,327 | todsacerdoti | 2024-11-06T21:01:36 | Fruit Credits: a personal accounting app based on hledger | null | https://dz4k.com/2024/fruit-credits/ | 1 | 0 | null | null | null | no_error | Fruit Credits: a personal accounting app based on hledger | null | Deniz Akşimşek
denizaksimsek.com |
I recently published the first pre-release version of Fruit Credits on Flathub.
(I then immediately published two more because the reviewer discovered a bug).
why: boring life stuff
After quitting my job and realizing it would be a while before I found a new one[1],
I realized I might need to be a bit more responsible with money.
Naturally, I downloaded hledger.
I’d tried to use hledger before but couldn’t make a habit of it.
I was concerned that my new attempt could be a procrastination mechanism,
but to my surprise, it was actually massively helpful.
Even after one month, I felt more in control of my life than ever before,
and it’s kept me in the black since.
It was hard for me at first to see the value of keeping your own books
when banks already record and present a history of transactions.
Right now, the benefits I experience are:
Everything is in one place.
Most people keep multiple accounts at multiple banks.
Banks are somehow all shit at application engineering,
or even just providing usable data export.
Before picking up hledger, I straight-up had no idea how much money I had.
The data is queryable.
I’m talking super basic stuff –
I’ve not even scratched the surface of what one can do with plain text accounting,
yet it’s still massively valuable to ask the computer
“how much did I spend on A since B?”
The usual reasons.
It’s free software operating on free file formats.
I can see my account balances without popups offering me loans I can’t pay back.
I started by typing transactions in the ledger format directly in a text editor.
Then, I started using hledger add, which has autocompletion capabilities.
Unfortunately, both of these were too clunky for me –
maybe it takes getting used to, but I kept making typos in hledger add
and fumbling as I tried to undo them.
I imagined a GUI for adding transactions quickly that wouldn’t require me to enter things in order.
This vision would eventually feature-creep itself into a GUI version of hledger.
how: exciting computer stuff
I’d heard Tauri was Electron but good,
which was an attractive proposition to a web developer.
It took me about a week to give up –
it turns out making a web app look good as a desktop app is harder Discord makes it look.
Being a GNOME user,
I was inspired by the many GNOME Circle apps to build something with GTK4 and libadwaita.
I fired up GNOME Builder and spent about 2 days paralyzed by the language selector.
After looking at code examples online, I decided Vala was the simplest option.
I used GNOME Builder to scaffold the app, and used it to develop for a while.
Eventually, I figured out how to build the app without it, and went back to Zed.
It took me a while to get everything configured –
for example, the default Builder template has a directory for translations with gettext,
but there’s extra setup required to actually build and use them.
However, between the build headaches, the programming inner loop was quite enjoyable,
especially with the new-ish Blueprint UI language.
vala is pretty cool, imo
It seems that Rust is the recommendation for new GTK apps.
Unfortunately, I never got into Rust, and
I don’t think adding the GObject memory management model into the mix would make it grow on me.
For the uninitiated, GObject is a subsystem that was originally part of GTK that
provides object-oriented programming and reference-counted memory management features in C.
Though GObject has some fun features like signals,
there’s little fun to be had in writing classes in C –
even with all the macro magic provided, class definitions are full of boilerplate code.
In addition, the reference counting mechanism requires you to g_object_ref () and unref your objects manually.
One feature of GObject is the ability to generate bindings to other languages,
including ones with actual OOP features,
which allows GTK apps to be implemented in a variety of languages.
However, these bindings often suffer from various impedance mismatches
between GObject and the languages’ own object models.
Vala, however, is implemented with GObject in mind from the start.
It has built-in syntax for features (like signals)
Though I’ve fallen in love with Vala, I can see why other people might not enjoy it.
It has unfixed compiler bugs, and a standard library that was written for C.
Most frustratingly though, refcounting is not as ergonomic as full tracing garbage collection,
and when using C libraries (including GObject based ones!)
that expect you to be comfortable with creative usage of memory,
you can run into hard-to-debug segfaults.
The de facto debugger for Vala is gdb, and it has no Vala support.
You have to debug through the generated C.
I had no gdb experience.
Thankfully, GLib has a built in logging framework that makes printf-style debugging quite comfortable.
What draws me to Vala is the feeling of consideration that it exudes from every orifice.
I would constantly discover that whatever I was trying to do,
the Vala designers knew I’d need to do it, and had implemented a feature to make it easier.
flatpacking haskell
A GTK app written in Vala and compiled with Meson is the happiest of happy paths for Flatpak.
Unfortunately, hledger, which Fruit Credits necessarily depends on, is written in Haskell.
Flathub requires not only that everything is built from source,
but also that all necessary sources can be downloaded before the build process (which is sandboxed).
This is antithetical to the model used by Haskell (and npm, and cargo, and many others)
where a build tool downloads dependencies and discovers transitive dependencies at the same time.
After a few days of digging through outdated resources, I understood that
a tool called cabal-flatpak was the best way to generate Flatpak manifests that
fetch and compile Haskell libraries in the right order.
Unfortunately, despite seemingly being under active maintenance, the tool had bugs,
and no discernible way to report them.
Since I don’t know Haskell, I couldn’t maintain a fork,
so I wrote some jq and shell to work around the bugs.
Upon submitting my app to Flathub, I found out I was something of a pioneer:
bbhtt: why are these needed and why do you need to do this manually?
dz4k: The haskell modules in the manifest are based on the output of
cabal-flatpak
and the article How to Flatpak a Haskell App into Flathub.
Are there more up-to-date resources for bundling Haskell programs?
bbhtt: No, we don’t have haskell apps. This is probably the second or third one ever.
– Add com.dz4k.FruitCredits by dz4k · Pull Request #5731 · flathub/flathub
fun.
what: thermonuclear war stuff
Though Fruit Credits is pretty barebones compared to what hledger can do,
it’s crossed from “dogfooding” to actually usable software for my use cases.
Queries and transaction input work well.
I’m currently working on a setup interface to create a new ledger file from scratch,
as well as making the app more beginner-friendly in general.
I’d like to add some way of editing and deleting transactions,
and some reporting features.
My usage of hledger is pretty limited in the grand scheme of things,
so I won’t be able to cover every use case by myself.
I’d love it if people gave Fruit Credits a try,
even if just to tell me how it couldn’t read their journal file.
get it on Flathub
get the code on Codeberg
complain about it on Codeberg
look at the website on the website
I’m fine – I live with my parents, have no dependents
and still have some income via Hypermedia Systems.
I would still very much like a job though, which is why
I invested in highly demanded skills as Vala programming and Haskell Flatpak packaging. ↩︎
| 2024-11-08T06:57:26 | en | train |
42,069,350 | mitchbob | 2024-11-06T21:03:01 | Between the Booms: AI in Winter | null | https://cacm.acm.org/opinion/between-the-booms-ai-in-winter/ | 2 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,069,378 | tombert | 2024-11-06T21:05:22 | NORAD Tracks Santa | null | https://en.wikipedia.org/wiki/NORAD_Tracks_Santa | 2 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,069,392 | kjappelbaum | 2024-11-06T21:06:07 | ChemBench: Evaluating LLMs Against Expert Chemists [New Results] | null | https://arxiv.org/abs/2404.01475 | 1 | 1 | [
42069393
] | null | null | null | null | null | null | null | null | null | train |
42,069,395 | gaws | 2024-11-06T21:06:10 | Fictional Brands Archive | null | https://fictionalbrandsarchive.com/ | 1 | 0 | null | null | null | missing_parsing | Fictional Brands Archive | null | null |
Sector:
Category:
Medium:
Genre:
Touchpoints:
| 2024-11-08T14:39:39 | null | train |
42,069,445 | tokai | 2024-11-06T21:10:10 | 1995 Flashback: First-time PC user can't work computer [video] | null | https://www.youtube.com/watch?v=0KDdU0DCbJA | 2 | 0 | null | null | null | no_article | null | null | null | null | 2024-11-08T13:37:39 | null | train |
42,069,453 | BUFU | 2024-11-06T21:10:39 | Ollama 0.4 is released with support for Meta's Llama 3.2 Vision models locally | null | https://ollama.com/blog/llama3.2-vision | 90 | 7 | [
42071467,
42070613,
42070824,
42070973
] | null | null | null | null | null | null | null | null | null | train |
42,069,479 | foweltschmerz | 2024-11-06T21:13:07 | The old man lost his horse | null | https://www.flaviaouyang.com/blog/daoism-parable-saiweng | 3 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,069,492 | gk1 | 2024-11-06T21:14:09 | Embed: Agentic AI for triaging security alerts | null | https://embedsecurity.com/ | 1 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,069,493 | rmason | 2024-11-06T21:14:17 | null | null | null | 1 | null | null | null | true | null | null | null | null | null | null | null | train |
42,069,546 | sandwichsphinx | 2024-11-06T21:17:41 | New SteelFox malware hijacks Windows PCs using vulnerable driver | null | https://www.bleepingcomputer.com/news/security/new-steelfox-malware-hijacks-windows-pcs-using-vulnerable-driver/ | 1 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,069,567 | NKosmatos | 2024-11-06T21:19:04 | Don McMillan – Nerdy Engineer stand-up comedian | null | https://technicallyfunny.com/ | 1 | 1 | [
42069589
] | null | null | fetch failed | null | null | null | null | 2024-11-08T08:50:43 | null | train |
42,069,580 | creaktive | 2024-11-06T21:19:45 | Metal Curl \M/_ | null | https://www.youtube.com/watch?v=atcqMWqB3hw | 1 | 1 | [
42071155
] | null | null | no_article | null | null | null | null | 2024-11-08T05:06:08 | null | train |
42,069,603 | geox | 2024-11-06T21:21:25 | Marijuana amendment didn't pass. But it got more than 50% | null | https://www.tallahassee.com/story/news/politics/elections/2024/11/05/florida-recreational-marijuana-amendment-failed-why/75989908007/ | 2 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,069,646 | gaws | 2024-11-06T21:23:50 | Passport Photos | null | https://maxsiedentopf.com/passport-photos/ | 315 | 49 | [
42070062,
42071211,
42070100,
42070771,
42070267,
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42070117,
42070335,
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42070118,
42069786,
42069847
] | null | null | null | null | null | null | null | null | null | train |
42,069,677 | gndk | 2024-11-06T21:25:58 | German coalition government collapses | null | https://www.euronews.com/my-europe/2024/11/06/german-coalition-government-collapses-chancellor-scholz-fires-finance-minister-lindner | 46 | 46 | [
42070709,
42069897,
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42069812,
42071225,
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] | null | null | no_error | German government on edge as Scholz fires his finance minister | null | null |
"He has broken my trust too many times", the chancellor said of his Finance Minister Christian Lindner.
The ruling German coalition has collapsed: Social Democrat (SPD) Chancellor Olaf Scholz has sacked Finance Minister Christian Lindner of the liberal FDP party.The leaders of what is known in Germany as the "traffic light" coalition - SPD, FDP and the Greens - had gathered at the Chancellery in Berlin in the evening. About an hour after the news was broken by several media outlets, Scholz faced the press and criticised his finance minister in no uncertain terms."He (Lindner) has broken my trust too many times", Scholz said, adding that there is "no more basis of trust for further cooperation" as the FDP leader is "more concerned with his own clientele and the survival of his own party," according to the chancellor.The coalition leaders meeting was widely reported as a "make or break" meeting for the coalition, with Lindner, in particular, having hinted in the run-up that he was not too worried about the latter.In his reaction to Scholz's scathing remarks, Lindner accused the chancellor of a "calculated break-up of the coalition" and his coalition partners of "not even accepting" the FDP's proposals for turning the economy around "as a basis for discussion".Discord about how to revive an ailing economyThe coalition had been at odds for a while, with serious strains on the budget for 2025 and a disappointing performance by the German economy eliciting increasingly different suggestions on how to face and solve the problems.Many coalition lawmakers had hoped that, after Donald Trump secured the US presidency once more, internal strife would be put aside to focus on the geopolitical challenges ahead.SPD leader Lars Klingbeil told German public broadcaster ARD in the morning: "I hope that everyone will now throw party tactics overboard, that everyone will look each other in the eye in the coalition committee this evening and realise once again what responsibility they now bear."Political analyst Thu Nguyen, Deputy Director at the Berlin-based think tank Jacques Delors Centre, said it was not meant to be in a comment on social media.Scholz also confirmed he would bring a no-confidence vote to the Bundestag by 15 January, paving the way for parliamentary elections by the end of March at the latest.Robert Habeck, minister for the economy and the green transition, said in a press conference on behalf of the Greens that, from their perspective, "it wasn't necessary that the evening ended like this", as suggestions for an agreement had been presented. He later added that, for the sake of stability, "we will now swiftly clear the way for new elections" while fulfilling the duties of government "in their entirety" until then. The article has been updated with the latest comments of leading coalition politicians. | 2024-11-08T17:30:04 | en | train |
42,069,695 | jnord | 2024-11-06T21:27:15 | The Low Fertility Fallacy: Why Panic About Birthrates Is Overblown | null | https://www.foreignaffairs.com/united-states/low-fertility-fallacy | 7 | 2 | [
42070336,
42070024,
42070276
] | null | null | null | null | null | null | null | null | null | train |
42,069,709 | sandwichsphinx | 2024-11-06T21:28:34 | Curl 8.11 Released with Official WebSockets Support | null | https://www.phoronix.com/news/cURL-8.11-Released | 1 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,069,758 | westurner | 2024-11-06T21:32:50 | Quantum Picturalism: Learning Quantum Theory in High School | null | https://arxiv.org/abs/2312.03653 | 2 | 2 | [
42070409,
42069788
] | null | null | no_error | Quantum Picturalism: Learning Quantum Theory in High School | null | [Submitted on 6 Dec 2023] |
View PDF
HTML (experimental)
Abstract:Quantum theory is often regarded as challenging to learn and teach, with advanced mathematical prerequisites ranging from complex numbers and probability theory to matrix multiplication, vector space algebra and symbolic manipulation within the Hilbert space formalism. It is traditionally considered an advanced undergraduate or graduate-level subject.
In this work, we challenge the conventional view by proposing "Quantum Picturalism" as a new approach to teaching the fundamental concepts of quantum theory and computation. We establish the foundations and methodology for an ongoing educational experiment to investigate the question "From what age can students learn quantum theory if taught using a diagrammatic approach?". We anticipate that the primary benefit of leveraging such a diagrammatic approach, which is conceptually intuitive yet mathematically rigorous, will be eliminating some of the most daunting barriers to teaching and learning this subject while enabling young learners to reason proficiently about high-level problems. We posit that transitioning from symbolic presentations to pictorial ones will increase the appeal of STEM education, attracting more diverse audience.
Submission history From: Lia Yeh [view email] [v1]
Wed, 6 Dec 2023 18:16:12 UTC (4,017 KB)
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42,069,834 | afandian | 2024-11-06T21:37:19 | Falsehoods Programmers believe about DOIs | null | https://pardalotus.tech/posts/2024-10-02-falsehoods-programmers-believe-about-dois/ | 2 | 0 | null | null | null | null | null | null | null | null | null | null | train |
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