For my children, I need to generate mindmaps from image taken from books.I want to use a frontier model (i.e. Claude, Mistral, ChatGPT, Grok...) but I want the most effective in terms of cost and quality.I was not impressed so much by Mapify or NotebookLM.Do you have some skills / suggestions / successful stories to share?
I think docker is not weird, it is very linear.
Docker has the merit to take a bunch of tools and to create an organized view of it.
I want to point you to Misterio https://github.com/daitangio/misterio which is a very thin layer on top of docker compose.
It simplify my homelab and works like a charm, and it is a waaaaay easier to use than k8s.
Thank you again, and let me know if you plan a trip in Milan :) for a meeting
https://github.com/daitangio/take-ai-control/tree/demo/nello...
branch demo/nello-openspec
Owning your email is nice, and save you sone bucks if you want family accounts.
I agree with you that these are not "trilling" discovery but they can be worth something anyway.
Like you, I do not like this news also because I think they will be used to just "push" the next two IPOs (Anthropic, OpenAI).
I'd prefer a K8s Operator or plugin (like Istio) to get all the pack.
When you are flooded by requests, predicting the right priorities and managing the Gantt is crucial.
Customer satisfaction is your priority as consultant, but wisdom is even more valuable, if you can apply it.
Bloated code is the critical problem. Once upon a time, I read C function
> char gets(char str);
is the first buffer overflow entry point, because it does not check the size of the destination buffer.
Sadly we cannot remove it from standard-C yet AFAI Know.
The success of Rust versus other languages is its secure-by-compile-time promise.
Also a lean java could help, but Java is so verbose/slow to start it bumps you away.
Is it time to give emacs another try
My two cents, but with real experience.
Can you add a feature like that or point me to a similar app?
The strongest competitors or a government law? I prefer the second.
Suppose Chinese model are super-risky: is it better to have them in the open, so everyone can review the problems or have a closed-model?
Security is better at open Linux is open source because development is better.
I understand Anthropic for sure, but trying to limit open weight model seems the opposite direction IF security+safety is the major concert.
The Genie is ALREADY outside the bottle, and there are already plenty of companies offering inference services
>Right now defenders are effectively banned from using Fable or Sol for cybersecurity because of Trump administration directives; that means the best alternative is using models from a country which has been trying to weaken our cyber defenses for years. This is insane!
I understand some guardrails are needed, but it is becoming increasing problematic manage them without a strong public discussion.
I mean before asking AI to implement :)
https://github.com/daitangio/take-ai-control
I need to study different harnesses CLI, and to find a way to switch from one provider to another.I built a template repository to govern your AI chats with more grip than usual. It leverages PI.dev coding agent and an aggressive isolation done with Docker + VS CodeSo far works on Linux and MacOS, and should also works on WSL2.The project uses a container to run your preferred AI harnesses CLI....
In all my professional carrier so far, a full rewrite without reason is a recipe for disaster, see
https://www.joelonsoftware.com/2000/04/06/things-you-should-...
This Joel Sposky article is always my northstar when I have such "temptations". My 2 cents
https://github.com/daitangio/take-ai-control
It is docker + vscode friendly. I tested it with major systems (copilot, codex, Claude Code and pi.dev) Comments Wellcome!
https://github.com/daitangio/misterio
It works very well!
SQLite is an embedded database: no socket to open, you directly access to it via file system.
If you do not plan to use BigData with high number of writers, you will have an hard time beating SQLite on modern hardware, on average use cases.
I have written a super simple search engine [1] using python asyncio and SQLite is not the bottleneck so far.
If you are hitting the SQLite limit, I have an happy news: PostgreSQL upgrade will be enough for a lot of use cases [2]: you can use it to play with a schemaless mongo-like database, a simple queue system [3] or a search engine with stemming. After a while you can decide if you need a specialized component (i.e. Kafka, Elastic Search, etc) for one of your services.
[1]: https://github.com/daitangio/find
[2]: https://gioorgi.com/2025/postgres-all/