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A tool that weigh the soul of incoming HTTP requests using proof-of-work to stop AI crawler. A beautiful idea againsta AI crazy bots -
Good Karma Kit
The Good Karma Kit is “a Docker Compose project to run on servers with spare CPU, disk, and bandwidth.” I like the idea in principle, but it is always a complex thing to do, because if you host unknown content, you can get in trouble easily (like pirated content or worst…)
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I want to share with you two tools I use a lot on some situation, where I need to write short manuals for co-worker or end-customers.
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We already discussed about Queue management solutions in the past, and I am always happy to write about it. Queue managers are not easy to implement, and there is a reson if IBM MQ Series is still a successful product. Some month ago, a big bank customer asked me to provide a small queue implementation to increase asynchronous internal processing of our payment solution.
The project had very strong contraints: I could not use existing queue system because they were not yet available, and I need to be able to provide microservice parallelism in a cloud-environent.
I have very little time to provide a solid solution, and re-inventing the wheel was not an option. Performance was important, but we plan to have a managable numbers of transactions per seconds, far behind modern cloud database capacity.
Challenge accepted.
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Sometimes you need to fix a production defect. To do it, you need to mirror a specific set of data, and do a test to proof the bug and then fix it.
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This week was very hot because Google published Gemma, a smal-size LLM, and also Nvidia has a rush on stock market, with a evaluation around 2000 BILLIONS dollars. Last but not least, Mistral.ai published a service for its more advanced model, which can also run on-prem.
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Generative artificial intelligence (GenAI) is artificial intelligence capable of generating text, images or other data using generative models, often in response to prompts.
Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics. This similarity is probability-based.
At low level, these systems are based on Transformer deep-learning architecture and are called generative pre-trained transformer (GPT) first defined in 2018 (for a complete description refer to Wikipedia)
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Modern computer are very very very fast. C/64 was about 1Mhz and you can hangs it just throwing a 10.000 cycle in interpreted BASIC v2 language.
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August throughts
Programming is like writing.Code must be easy to read, and understand. For this reason I do not like very much technologies like:
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I the last years I decided to put a stop on my RasperryPI2 and have a mini pc always on.
It is a very slow centrino with 2 CPU threads and 8Gb of RAM, a huble micro SATA SSD and works great with Debian Linux. I use it as a benchmark to understand if a project is worth studying: if the CPU Load stay low, the code is very well written.
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A friend of mine asked some insight on how to harden a Gitea server on Internet. Gitea is a web application for manging git repositories.
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Bullet points:
- 1979: Unix V7 Introduced the chroot command to isolate the filesystem a process "access" to.
- Various technology was introduced up to 2006, like Virtuozzo (which patched Linux in a proprietary ways)
- 2006: Process Containers Launched by Google in 2006 was designed for limiting, accounting and isolating resource usage (CPU, memory, disk I/O, network) of a collection of processes. It was renamed “Control Groups (cgroups)” a year later and eventually merged to Linux kernel 2.6.24.
- 2008: LXC LXC (LinuX Containers) was the first, most complete implementation of Linux container manager. It was implemented in 2008 using cgroups and Linux namespaces, and it works on a single Linux kernel without requiring any patches.
- 2013: Docker Docker used LXC in its initial stages and later replaced that container manager with its own library, libcontainer. Docker offered a way to configure and manage containers, i.e a standard de-facto for this technology. As you see Docker was based on cgroups and LXC, seven-years old technologies
- On September 2014 Google published the first release of Kubernetes
- In 2015 Docker, CoreOS and others founded the Open Container Initiative's (OCI). K8s does not need docker anymore to work, but Docker traction is still strong.
References:
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NILFS is a log-structured file system supporting versioning of the entire file system and continuous snapshotting, which allows users to even restore files mistakenly overwritten or destroyed just a few seconds ago.
Discussion on Hacker NewsNILFS was developed by NTT Laboratories and published as an open-source software under GPL license, and now available as a part of Linux kernel.
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Nice tool to test:
Git hook scripts are useful for identifying simple issues before submission to code review. We run our hooks on every commit to automatically point out issues in code such as missing semicolons, trailing whitespace, and debug statements. By pointing these issues out before code review, this allows a code reviewer to focus on the architecture of a change while not wasting time with trivial style nitpicks.
As we created more libraries and projects we recognized that sharing our pre-commit hooks across projects is painful. We copied and pasted unwieldy bash scripts from project to project and had to manually change the hooks to work for different project structures.
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