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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