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er captures a broad set of system metrics to ensure smart drive metrics are included run sudo pmlogconf r var lib pcp config pmlogger config default when prompted enable the s m a r t drive statistics linux group this ensures drive health metrics are captured continuously allowing you to review historical trends using tools like pmchart pmrep or grafana with the pcp valkey datasource with pmlogger running you build a historical record of your drive health if a drive starts failing in six months you can look back and see exactly when the warning signs began conclusion drive failures give warnings through smart metrics error logs and performance degradation with pcp s drive monitoring capabilities you have the tools to catch these warnings before they become data loss in this post we covered setting up the smart pmda for universal drive health monitoring interpreting key metrics like temperature wear levels error counts and tracking drives reliably with wwid based identifiers nvme users can go deeper with error log collection and seagate drive owners have access to extended farm telemetry combined with grafana dashboards and pmlogger you get continuous visibility into your drives health the key takeaway continuous monitoring catches trends that one time checks miss a few minutes of setup today can save hours of recovery work or worse unrecoverable data loss tomorrow for more information visit the performance co pilot documentation and the grafana pcp plugin documentation the grafana dashboard used in this post is available to download here and can be imported via dashboards import in grafana the pmie alerting rules are also available to download here and can be copied to etc pcp pmie for use with pmie no ai posted by remi collet remi on 2026 08 07 09 42 00 utc because artificial intelligence competes with humans e specially for our planet s resources i ve chosen my side the human i also think that ai is terribly bad for what should be our priorities ai doesn t stop wars ai doesn t help with the climate crisis ai doesn t solve the world s nutrition problem ai doesn t reduce the hate between humans ai doesn t improve the distribution of wealth ai doesn t enhance education ai doesn t contribute to open source ai doesn t create everything on my website in my repository in my personal work and my contributions to the open source community is the result of my skills and my experience no ai is used no ai will be used yes i m aware that ai may help in some projects e g medical diagnostics live translation but i don t think the benefits outweigh the costs yes this is very political you can read artificial intelligence controversies or why no ai misc fedora bits first week of aug 2026 posted by kevin fenzi kevin on 2026 08 08 20 58 59 utc another saturday spent dealing with scrapers so now time for a recap of the previous weeks events scrapers i am not sure if they have some reason for hitting on saturday mornings but they did so again this week once again targeting src fedoraproject org which is unfortunate in that it s a single backend server without much ability to scale horizontally running rhel8 and since we are moving away from it someday soon we don t want to spend too much time on it so the two approaches left for me here are block filter delay cache at the proxy level to keep traffic managable make the single backend process requests better faster to keep up i tried a number of things this time and ended up with a combo some things increased and blocked on proxies and the backend tweaked and given more cpu memory i m not sure if this is going to last but for now things seem to be back to normal i am hopeful we can scale forgejo much better here it s running in openshift and we have a lot more options there rhel10 migrating a bunch more hosts done this last week i have about 3 more easy ones to finish up and then we will get to ones that will need an outage database servers vmhosts that host important services etc next week is fedora 45 branching so will keep things quiet but i am tenatively thinking about a mass update reboot cycle rhel10 migrating things the week after will see how much i can line up next week it would be good to get as much done as we can before we head into freeze the week after the rest of the week has been a blur as always comment on the fediverse https fosstodon org nirik 117061938567047801 when your code starts seeing ghosts debugging silent memory faults posted by rénich bon ćirić renich on 2026 08 07 13 00 00 utc let us be real for a moment we have all been burned by ai hallucinations you sit down ask an llm to write a helper script or refactor an awkward class and it hands back something that looks strikingly professional the indentation is crisp variable names are elegant and docstrings read like poetry you compile it and kaboom the model confidently imported a non existent package called an api method that exists only in its imagination and invented three cli flags out of thin air it stared right into your terminal and insisted with absolute conviction that everything was tested and ready to ship if you have ever caught an ai model quietly lying about compiler output you know the feeling trusting an llm to blindly hallucinate its way through code generation is a speedrun to broken builds and late night debugging sessions warning blindly copy pasting llm code without verifying imports checking types or running strict linters is how phantom dependencies and swallowed exceptions sneak into production reframing the phantom the standard industry reaction is to clip the model s wings turn down temperature tighten context windows and treat every output with extreme suspicion yet the core issue might not be that the ai sees ghosts but where we tell it to look when you force a language model to hallucinate code logic it invents fake syntax but when you ask it to hallucinate a human being the dynamic shifts completely meeting gus the 15 year principal sre instead of prompting an ai assistant to write a module step back and ask it to imagine someone using your software let us call him gus gus is a 15 year veteran principal infrastructure architect he is tired he has seen three cloud migrations come and go he hates over engineered abstractions and he just wants his scripts to run fast without breaking his weekend now instead of asking the ai to write functions you ask it to step into gus s boots and map out his day to day pain what makes gus swear at his terminal why did his command hang for 300 seconds without emitting a single log line where will he get stuck why does passing an empty string force him to guess positional arguments like it is 1995 what makes him throw his coffee mug why did a background job die silently when his laptop went to sleep suddenly the ai stops fabricating imaginary library calls instead it starts uncovering genuine operational friction points edge case hazards and ergonomic traps that you the human developer were too close to the code to see a disciplined protocol persona simulation let the model vividly imagine the user attempting real messy tasks across hundreds of heterogeneous servers list every single friction point the problematic register catalog every issue into a master friction list identify the user s symptom locate the technical root cause and draft raw discussion notes peer debate no code allowed debate each friction item as equal engineering partners explore security trade offs check standards compliance and test alternative ideas the golden rule do not touch source code yet immutable adrs once you align on the design codify the decision into an architecture decision record adr document the context considered options decision outcome and trade offs permanently note recording decisions in architecture decision records adrs ensures that future maintainers understand why a trade off was made protecting the design from architectural decay guarding against sycophancy the quiet hazard in persona simulation is sycophancy the model s default instinct to tell you your ideas are brilliant when an ai partner reflexively agrees with your proposals you re completely right the simulation breaks bypassing this trap requires enforcing a strict peer consensus rule the ai is required to critique your logic point out security flaws and expose edge case failure modes no design is finalized until both human and ai reach genuine verified mutual agreement every proposed specification must survive an aggressive adversarial attack pass that actively tries to break the architecture before any documentation is written tip if your ai assistant agrees with your architectural proposal on the first try ask it to launch an adversarial attack pass on its own recommendation you will be amazed at what turns up redirecting the engine generative models remain engines of probabilistic imagination treating them as deterministic compilers is a fundamental mismatch when that imagination is redirected toward human empathy operational friction and adversarial edge cases hallucination ceases to be a liability it becomes an architectural asset references pelican static site generator architecture decision records adrs restructuredtext guide referencias multiplexores de terminal en fedora más allá de tmux y screen friday links 26 25 posted by christof damian cdamian on 2026 08 06 22 00 00 utc this week i really enjoyed the podcast about compression the hutter prize and the one about seashells quote of the week the thing about the future is that it keeps happening without you normal warren ellis leadership board meetings your notetaker is not welcome here this might be true for more meetings migration of fedora scm requests from pagure to forgejo posted by fedora infrastructure status admin on 2026 08 05 12 00 00 utc the fedora scm requests ticket queue is being migrated from pagure io releng fedora scm requests to forge fedoraproject org releng fedora scm requests as part of this migration fedpkg has been updated to file new requests against forgejo instead of pagure toddlers the automation that processes these requests and performs the actual dist git operations creating running ollama locally with podman on fedora linux posted by fedora magazine admin on 2026 08 05 08 00 00 utc running large language models llms locally has become increasingly popular for development privacy and offline testing ollama makes this incredibly straightforward allowing you to run models like llama 3 or mistral directly on your machine by leveraging podman on fedora linux you can isolate ollama inside a container this approach keeps your host system clean while making it effortless to spin up manage and tear down your ai development environment what is ollama ollama is an open source framework designed for running creating and sharing large language models it packages model weights configuration and data into a unified management system running it inside a container means you don t have to deal with complex local dependencies python environments or complex gpu driver configurations on your base os verify or install podman podman is available by default in fedora workstation it can be easily install if missing using dnf sudo dnf install podman y for fedora linux silverblue users podman is natively available in the immutable base system and no extra steps are necessary to verify your installation and ensure everything is running smoothly execute a quick check podman version step 1 create a persistent volume for your models llm weights can be huge often ranging from 4 gb to over 40 gb depending on the model size to avoid downloading these models every time you restart your container create a persistent podman volume to store them safely on your host disk podman volume create ollama_storage step 2 run the ollama container next spin up the ollama container the following command pulls the official image attaches the volume we just created and maps the communication port 11434 to your host machine podman run d v ollama_storage root ollama p 11434 11434 name ollama ollama ollama note on hardware acceleration the command above runs ollama using your cpu if you are on fedora workstation or silverblue and want to pass through an nvidia gpu for fast hardware acceleration make sure you have the nvidia container toolkit installed and append the gpu flag device nvidia com gpu all step 3 download and run an ai model with the container running in the background you can interact with it using podman s execution command let s pull and run llama 3 a highly capable lightweight model perfect for local development podman exec it ollama ollama run llama3 the first time you execute this podman will download the model weights into your ollama_storage volume once the download completes you will be dropped directly into an interactive terminal prompt send a message for help tell me a fun fact about fedora linux fedora linux is named after the iconic felt hat worn by the red hat shadowman logo it started as a community project to provide extra packages for red hat linux to exit the interactive prompt simply type exit step 4 interact with the local api because we mapped port 11434 to our host system you can also interact with your local ollama instance via its built in rest api open a standard terminal window and send a curl request curl http localhost 11434 api generate d model llama3 prompt why use containers stream false this returns a structured json payload containing your answer allowing you to easily hook your local model up to web apps scripts or ide extensions checking container status to monitor your running local ai instance use the classic podman management commands perhaps starting with podman ps you can also inspect the logs to make sure the api server is listening properly podman logs ollama when you are done with your development session and want to free up system memory stop the container podman stop ollama if you ever need to completely remove the container environment use podman rm ollama note your downloaded models are completely safe inside the ollama_storage volume and will instantly reattach the next time you spin up the container conclusion using podman to manage ollama on fedora linux or fedora silverblue offers a clean containerized way to build and test applications with llms completely offline it bypasses host environment pollution isolates large model storage cleanly into a named volume and treats your ai stack exactly like any other microservice new badge froscon 2026 attendee posted by fedora badges admin on 2026 08 05 04 49 30 utc you visited froscon 2026 the future of gnome boxes posted by felipe borges feborges on 2026 08 03 11 28 49 utc i have spent the last two years rebuilding gnome boxes from the ground up driven by three main factors i spoke extensively about this effort in my recent linux app summit guadec 2025 and 2026 talks but today i am excited to share the result for general testing first shifting to a fla...
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