At the beginning of the year, I bought an NFC smart lock for the front door for 170 bucks. Recently, I wrote a review on Amazon stating that the batteries lasted only a month and a half, and if it continues like this, I will end up paying almost the same amount annually. The manufacturer has responded saying they will refund the money. They didn’t ask to remove the review, and I don’t even know if that’s possible.
Update: figured it out, looks like the lock needs 6v + 6v for different purposes. Maybe the power part and electronics.
Anyone who knows electronics, help me understand. Red wires are connected to contacts that respond to the tester. A total of 8 batteries. I can’t see a classic snake configuration here. Can’t understand why the lower right ones are responding. I want to connect an external adapter
Looking for alpha-testers. As part of R&D and for my own tasks, I wrote a productivity tool (I actually wrote about this in my last post, but Facebook said that because I put a link in the post, only 12% saw it). Now I want to check if it will be useful to anyone else. If the idea resonates with you — let me know, and I will share access.
Website smartfolio dot me. What’s the main idea?
It’s an online notebook for working with text and PDFs, organized as a graph. It looks like Google Docs, but there’s an important difference: you can attach “child” documents to specific parts of the main text to expand on details or clarify concepts. These “comments” themselves are full documents and can have their own nested branches.
If there’s a fragment in the text that is unclear, you can ask the system to explain it (this will require your Google Gemini API key).
The system uses the full context of the document to generate a response.
Explanations are permanently attached to a specific place in the text.
This is super convenient when reading complex scientific articles. For instance, you can highlight the authors’ surnames in a PDF and instantly get a background on them — the information will be attached right to that fragment on the page.
Typical workflow
Upload a complex text and read it right in the app from either a mobile or a computer. As you go, add manual or AI-generated notes to important or unclear sections for future reference.
I do not store your documents, PDFs, images, or API keys on my servers. All data is stored in Turso DB (SaaS, free up to 5 GB).
Screenshots on the website’s main page best describe the project.
How to try?
To register in the app, you need an invite code. Just write me in the comments or in a private message, and I will send it.
Website smartfolio-dot-me
Can you guess what this is?;)
I assembled a plotter from a kit. It’s practically a Lego set – you spill out the parts from the box and then read the manual. It worked right away. I have some ideas about what to do with this thing, I’ll tell you sometime.
It would be interesting to have a camera somewhere on the end of the phone, so you could film more or less covertly.
How unexpectedly useful Gemini turned out to be in a simple task – to create a high-quality PDF from a low-resolution preview. Nano Banana Pro was used, meaning, the output was raster, not vector. Look at the difference. Very often it is impossible to even make out the text, so from time out it turned into time dute;-). But overall, not bad.
Veritasium released a very cool report yesterday from ASML about the equipment used to print chips for your little phones, cameras, and laptops.
For those who aren’t familiar with the process. First, a monocrystal is grown from ultra-pure silicon and cut into thin wafers, then multiple layers of thin dielectrics, conductors, and semiconductors are repeatedly applied to the wafer surface, each time shaping the necessary areas using photolithography, etching, and ion doping, eventually creating billions of transistors and connecting metallic paths; finally, the wafer is tested, cut into individual crystals, and packaged into casings, making them into finished microchips.
This process had a limitation – the width of the paths and the distance to the next one are limited by the wavelength of the light used, and reducing it is difficult because there’s nothing to focus such a beam with – lenses simply absorb/reflect everything. In EUV lithography (extreme ultraviolet), the wavelength is 13.5 nm. This is virtually soft X-ray radiation.
The video explains details about the ASML machine costing 400 million dollars. Instead of refracting lenses, highly complex systems of reflecting mirrors are used. These mirrors are the smoothest surfaces ever created by humanity. If the mirror of this machine were enlarged to the size of the Earth, the largest bump on it would not be thicker than a playing card. To enable the mirrors to reflect X-rays, up to 76 alternating layers of tungsten and carbon, each less than a nanometer thick, are applied. All this is done by Zeiss. In addition, this mirror has a controlled curvature—it is constantly adjusted by robots with precision up to picoradians. The precision of the mirror control is so high that if a laser were mounted on it, directed at the Moon, the system could choose on which exact side of a 10-cent coin lying on the moon’s surface to hit with the beam.
But. We don’t have a “light bulb” that emits light in the EUV range.
To generate this light, a laser “shoots” at a droplet of molten tin the size of a white blood cell, traveling at 250 km/h. The first pulse flattens the droplet into a disc, the second and third turn this “disc” into plasma – and all this occurs within just 20 microseconds. When hit by the laser, the droplet heats up to 220,000 Kelvin — approximately 40 times hotter than the surface of the Sun. This plasma emits that very necessary light. And it does so 50,000 times a second. They say it’s been brought up to 100,000. Imagine, at a hundred thousand laser shots per second, it never misses a single one. All this happens in a deep vacuum. To clean the mirrors from tin particles, the chamber is constantly blown with hydrogen at a speed of 360 km/h — faster than a Category 5 hurricane. This process is described by the same formula (Taylor-von Neumann) that describes a nuclear explosion or supernova explosion.
The machine layers the chip with an error margin of no more than five atoms, while the matrix swings back and forth with an overload of 20G.
A single High-NA machine is transported in 250 containers on 25 trucks and seven Boeing 747 aircraft.
Link to the video – in the comments. Or search on YouTube on the channel veritasium.
What I did on the plane to/from vacation and sometimes in between: 3D visualization and editing volleyball schemes for Nadya (she’s a coach). This court in the attached image freely rotates, players can be placed on it, and the ball and player paths are shown – all in 3D.
The ball’s trajectory is calculated so that it does not cross the net when moving from A to B (Bezier formula). Players can take several poses – right now there are hastily made poses for serve, attack, block, pass/receive. Interestingly, in the code: I had to write a bit of “volleyball brains”. The system itself calculates the ball’s trajectory through Bezier curves so that it always passes over the net. Moreover, the height of the launch depends on the type of action: for an attack, the ball “launches” from a higher point than for a pass. I also added auto-rotation: the 3D model itself turns its face to where, according to the scheme, it needs to pass or run.
The longest and most difficult task was creating the 3D model of a female volleyball player. To generate a realistic volleyball player, I used the tripo3D service. It gave me a model in a neutral pose (for free). Theoretically, you can then use Blender and the Rigify plugin to attach an armature to it and move its arms and legs, which would recalculate the model.
However, in reality, this approach does not work well: the AI-generated model contains a large number of geometric errors, which the renderer forgives but Rigify does not. They can be roughly divided into two types — incorrect polygon normals and issues with non-manifold geometry, which are significantly more challenging to fix. Inside the body, there may be “floating” clusters of polygons or intersecting surfaces. When Rigify tries to calculate weights (which bone affects which part of the skin), this internal noise confuses the algorithm, and as a result, the weights are distributed chaotically (for example, moving the arm might start pulling the mesh on the stomach). Plus, the model is slightly asymmetrical.
Non-manifold is a geometry error where the topology of an object ceases to be correct in terms of a three-dimensional body: edges may belong to more than two polygons, polygons may only touch at vertices or edges without a common volume, and “hanging” surfaces or zero thickness may appear inside the model. Such geometry formally does not describe a closed volume, causing problems with rigging and deformations. Moreover, the model needs to be simplified because millions of polygons are not needed for rendering in real-time in a browser.
I fixed these using MashLab, additionally refining by hand (“with a file”). In the end, the model turns out slightly different from the original almost everywhere. The original model had “skin” in the form of textures – the face, shirt, and shorts had to be colored. How to transfer all this to a simplified model? For that, there’s a special operation in Blender called Baking. This also involves some tricks. In the end, it didn’t transfer perfectly, but perfection isn’t necessary yet.
Next, we attach the armature to the “joints”, and after about three hours of figuring out why everything does not work as it should, it finally worked. I made four poses, and now each circle (player) can be told which pose it is in.
I’ll also need to make dynamic changes to the uniform colors – that shouldn’t be difficult. There’s also an idea to transfer poses from photographs – this is more complicated, but generally feasible. Using MediaPipe/AlphaPose, you can detect key points in 2D, then some models like HMR/HybrIK can “lift” flat coordinates into 3D space, outputting relative joint rotation angles. The resulting data can be attempted to be projected onto a Rigify skeleton. Since the proportions of the generated volleyball player and the person in the photo may not match, that’s exactly why Inverse Kinematics (IK) is used. This part is quite complex, but overall it’s not strictly necessary – just interesting to figure out and make something functional.
Video in the comments
Hi, I'm Rauf Aliev. 