If you’ve tried vibe-coding, or even made it part of your regular practice, you could not help but notice - it is pretty hard to instruct these freaking LLMs to do what you want! They do work wonders, but they are notorious for being sloppy and not following instructions well. That’s partly how they (currently) work, which the tech-sphere argues is solvable. But the challenge is much bigger, and harder to solve, than that! ...
I am no fan of LabVIEW for multiple reasons, but the truth is, it is hard to replace when it comes to instrument control. Installing it on a Debian machine (e.g. Ubuntu) is possible despite not being officially supported by National Instruments. Here is how to do it. Comment out the line in ./INSTALL script that makes installation of niwebpipeline necessary for success: #webpipepkg="$webpipebase-$NIWEBPIPELINEVERS.i386.rpm" Install nisyscfg-runtime manually: cd ./dists/nisyscfg-runtime-18.0.0f0 sudo bash ./INSTALL "--no-prompt --accept-license --nodeps" Comment out corresponding section in ./INSTALL script (l. 1025-1030): ...
The overview picture of operation of LoRaWAN network ([source](https://www.thethingsnetwork.org)) In this post, I will tell you about an exciting IoT device me and my team prototyped at MakeZurich week-long makerdays. Keep reading if you are a maker, IoT enthusiast or a person interested in using IoT principles in a real world scenario. MakeZurich MakeZurich is the “Civic Tech and LoRaWAN Hackdays for a better city”. It is organized jointly with the city administration to explore new ways of solving challenges the city is facing with the help of open networks and civic tech. I missed the first run, that took place last year, because I found out about the event too late. This year, I had it in my calendar and was ready to take part. The event had a smell of a hackathon: difficult and open-ended challenges to be tackled in innovative fashion in too little time in small teams, mostly with people you didn’t know before the kickoff. But it was more than that. Rather than hackathon,it were Makerdays or Hackdays. It lasted for full 8 days, and on each day a makerspace was open so that I could just drop by, ask people about technical issues I was having and hack on the prototype and code for our project. In retrospect, I view the event as great learning experience that gave me quick insight into the field of IoT and LoRaWAN. (Big thanks to the organizers for all their work, it showed!) ...
In this post, I will demonstrate and explain Reinforcement Learning code I developed. You will learn how one can train an AI agent to master Atari games and understand the technology behind DeepMind’s AlphaGo, the first computer program to defeat professional human Go player. This will be fun and, surprisingly, simple, so let’s dive in. OpenAI Gym OpenAI’s gym is a toolkit for developing and comparing reinforcement learning algorithms. Big standardized datasets have proven pivotal in development of deep learning algorithms. Similarly, collection of test problems, environments, as provided by gym aids in evaluation of reinforcement learning algorithms when an agent learns to take actions in response to observations from environment to “solve it”. All that comes below is enabled by the work of guys at OpenAI. Hat tip to them. ...
I am sure that most of you have heard about many of the deep learning libraries out there including TensorFlow, Theano, Keras and PyTorch. They facilitate building of layered and potentially complex neural network models for areas as diverse as automatic image captioning, speech recognition, and drug design. Undoubtedly and for better or worse, the deep learning field is riding on a wave of hype. The good thing about it is definitely that the deep learning libraries keep developing at astonishing pace. In this article, I will make use of these developments to implement two stochastic models from systems biology and solve them efficiently using the computational graph paradigm of Theano and TensorFlow. ...
Source Introduction Genetically engineered cells and organisms are being used to produce array of commonplace commercial products including [drugs](http://www.madehow.com/Volume-7/Insulin.html) and [materials](https://www.bio.org/articles/current-uses-synthetic-biology). Biological engineering is being employed to enhance nutrition content of various foods and yield of crops. Recently, [lab-grown meat](https://en.wikipedia.org/wiki/Cultured_meat) represents an interesting alternative to traditional production and [synthetic biology](https://www.nature.com/news/2010/100120/full/463288a.html) is being [explored even by artists](https://www.ginkgobioworks.com/2018/04/11/creative-in-residence/). These successes are, however, just baby steps in face of the richness and complexity of "products" nature is so adept in engineering. Like what you ask? Like YOU, for example. In the meantime, development is ongoing at rapid pace. We are getting finer understanding of molecular-scale processes by which the life is implemented and by extension, we are becoming more adept in exercising control over them. Biology is becoming easier to analyze and design using established engineering approaches. At different system levels, from molecular to cellular to populational, mathematical modeling and abstraction in design are becoming possible. (If this abstraction thing is too abstract for you, think about this as if in your Arduino project (like this one) you had to build and debug the microcontroller first. Before you could do that, you would have to do the same for all the resistors, transistors and other elements of the board. Instead, and luckily, you just need to have the board shipped and plug it into your laptotp to boot it up. You don’t have to think of all the possibly very intricate details that make it work and can focus on building some application on top of it. This is the power of abstraction in design when put to work.). ...