The initial source comes from sdcuike/issueBlog#4
https://github.com/PacktPublishing free to download books code by Packet
https://github.com/EbookFoundation/free-programming-books Very immense
https://github.com/PacktPublishing free to download books code by Packet
https://github.com/EbookFoundation/free-programming-books Very immense
original code: https://github.com/Kamil-Krawiec/piper-tts-http-server/blob/master/server.py
i simplified the code and add some features:
TensorrtExecutionProvider, CUDAExecutionProvider, MIGraphXExecutionProvider, OpenVINOExecutionProvider, CoreMLExecutionProvider, DmlExecutionProvider, CPUExecutionProviderTested on one machine: Dell Pro 14 Premium PA14250 (Core Ultra 7 266V, Lunar Lake), BIOS 2.19.0, Ubuntu 26.04.1, kernel 7.0.0-43-generic, Secure Boot enabled, 2026-11-13. Should be close for other Lunar Lake laptops with the same chain (OV08X40 behind a Synaptics SVP7500 CVS bridge into IPU7), e.g. Dell Pro 13 Premium PA13250, but that is untested.
Only Canonical-signed kernel modules are used. No MOK enrolment, no DKMS, no Secure Boot changes.
| # Standalone Nix flake packaging Tern 0.2.4 (https://tern.sh) from upstream's | |
| # prebuilt glibc release tarballs, for x86_64-linux and aarch64-linux. | |
| # | |
| # Usage: | |
| # place Tern-0.2.4-linux-x86_64.tar.gz (and optionally -arm64.tar.gz) from | |
| # the Tern release next to this flake, then: | |
| # nix build | |
| # nix run | |
| # | |
| # If this directory lives untracked inside another git repository, Nix's git |
| #EXTM3U | |
| #EXTINF:-1 tvg-id="bbb1" tvg-name="BBB 26 - 1",BBB 26 - 1 | |
| https://d1muf25xa03so8hp29.s27-usa-cloudfront-net.online/token/1869c00ac19ae3edc0b911dbf9fb71e8/bbb1.txt | |
| #EXTINF:-1 tvg-id="bbb2" tvg-name="BBB 26 - 2",BBB 26 - 2 | |
| https://d1muf25xa03so8hp29.s27-usa-cloudfront-net.online/token/69466fbe3173710b98acff391cbf6531/bbb2.txt | |
| #EXTINF:-1 tvg-id="bbb3" tvg-name="BBB 26 - 3",BBB 26 - 3 | |
| https://d1muf25xa03so8hp29.s27-usa-cloudfront-net.online/token/6e66cc0a9123b0f3b8c3427b9ff89829/bbb3.txt | |
| #EXTINF:-1 tvg-id="bbb4" tvg-name="BBB 26 - 4",BBB 26 - 4 | |
| https://d1muf25xa03so8hp29.s27-usa-cloudfront-net.online/token/0f8dfc8c04a0a5a5d5b19917e3fe791b/bbb4.txt | |
| #EXTINF:-1 tvg-id="bbb5" tvg-name="BBB 26 - 5",BBB 26 - 5 |
Thanks to everyone that made this possible:
grimdoomer: For the Bad Update exploit
InvoxiPlayGames: For the FreeMyXe project
| /* ## Screen Reader Text | |
| --------------------------------------------- */ | |
| .screen-reader-text, | |
| .screen-reader-text span, | |
| .screen-reader-shortcut { | |
| position: absolute !important; | |
| clip: rect(0, 0, 0, 0); | |
| height: 1px; | |
| width: 1px; |
| # Find budle ca store for updates: | |
| grep -i "AUTOUPDATECERTSDIR=" /usr/syno/bin/update-ca-certificates.sh | |
| # Remove the compromised root certs | |
| rm /var/db/ca-certificates/emSign_Root_CA_-_G1.crt | |
| rm /var/db/ca-certificates/emSign_Root_CA_-_C1.crt | |
| rm /var/db/ca-certificates/emSign_ECC_Root_CA_-_G3.crt | |
| rm /var/db/ca-certificates/emSign_ECC_Root_CA_-_C3.crt | |
| rm /var/db/ca-certificates/Comodo_AAA_Services_root.crt |
These results are with glibc malloc on x86_64. The last public PaX and grsecurity patches don't support arm64 which is one of the two architectures (x86_64 kernels including x32/x86_32 and arm64 kernels including armv7 userspace) focused on by linux-hardened. There isn't anything other than x86_64 to compare across all 3 kernels although linux-hardened has the same end result for both x86_64 and arm64 (with slightly different starting points) and there are few mainline differences. The linux-hardened implementation of ASLR is a very minimal modification of the mainline implementation to fix the weaknesses compared to grsecurity. The intention is to upstream all of these changes, although care needs to be taken to properly justify them to avoid getting anything rejected unnecessarily.
Explanation of differences between kernels:
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.