Conference

I am extremely grateful to Linux Foundation for giving me the opportunity to attend Open Source Summit India 2026. I learned a lot and met a lot of new people out there, I tried to log my learnings throughout using some makeshift notes. Until I write a proper draft I will post whatever I wrote during the 2 day event. Day - 01 Key Note 01 Consumption -> Global Leardership LFX insights: look into this PARAS under CDAC Agentic AI, tokenomics, OCUDU Key Note 02 From Bots to Agents(Reactive to Autonomous) Key Note 03 Linus PR should have explanation because nobody looks at code now Linus hasn’t in decades Git+Email+Google more than enough for everything, work with people not with tools OS operates on Trust Java version of Git Logic errors -> not memory safe Kernel development -> start with drivers A lot of efforts getting wasted in bug reports due AI slop Suggestive patch, a human element during bug report Zero to demo LLM works here but Zero to prod? Main question lies here LLMs mostly for demo stuff Key Note 04 DPDAA coming in India, basically a privacy enforcement policy Trust registry Resolution is 792(Iata) Hey Yocto Buid me a custom linux Customized opinionated Linux Distro LTS releases for embedded After 5.3 things changed For a linux image need: Bootloader A kernel image ARM/RISCV(need a Device Tree Blob DTB - for laying out hardware maps) root file system(clib, init, apps) Poky dont use because it is not as secure distro is a superset of image, image is customized according to scenarios BusyBox: goto for embedded linux BB: BitBake I break things AI fixes them: Workflows good at automating but not at fixing stuff hence agentic Agentic CI Traditional CI: Fails -> Notifications on error but in Agentic AI: Fails -> Automated PR+RCA GitHub Actions as trigger + LangGraph as orchestration + Gemini as reasoning engine + Docker Full observability is important in LangGraph Following are measure for secure operations Human in the loop is a must here Secure execution and validation https://github.com/PR3MM/ Cannot fix any architecture level of failures CI/CD Basics: Choice fatigue for tools Main question arises where to actually start Modern development consists a lot things to keep in check API first development break the dependency change Monolith vs Microservices Monolith obviously easy to build but not really scale-able APIs + Microservices + Containerization + CI/CD Tekton Architecture: Kubernetes yaml files something for CI ArgoCD: GitOps API server Repo server App controller Redis cache Using ArgoCD makes your deployment declarative API Gateways Which API should we use? REST GraphQL: flexible frontend queries Event driven But what if an event driven system a operation is supposed to be canceled later? Scaling issues: instead of traditional scaling use kubernetes In kubernetes we have got pods In serverless we have to make a request CNCF scaling tools: Knative, Karmada, Karpenter all are kubernetes native https://landscape.cncf.io Graduated and incubating CNCF projects are good ones as they have more support from developers Recipes and Runtimes: Basic concepts on Containerization Provenance? Supply chain poisoning? chroot: sys-call to change process of a child, first instance of process isolation FreeBSD Jails -> Solaris Containers -> LXC linux containers cgroups(control groups what resources each process uses), namespaces Containerizing our micro-services? -> micro-bakery? For Containerizing need reproducible environment, recipe Image hardening to reduce the attack surface remove the unnecessary packages Multi-stage builds to reduce file size of docker image for them Using either alpine as a base image, but for python you will have to see a lot of workarounds or just distroless, but in here things can be complex There is also a third approach aka chisel Rockcraft another OCI(open container initiative) compliant tools declarative not imperative like bash pebble process orchestrator chisel -> basically ubuntu but with just what is needed .rock file is oci archive Multipass great way to spin-up a VM Without chisel still there is filesystem in image for any attacker to exploit Now with rockcraft we have a bare base Without bash, how does one debug a chisel image? for this we coreutils_bins in yaml To avoid container escape run as non root user preferably using podman, since docker by default needs root permission Journey of a packet: For getting the ping of both the nodes: tcpdump -eni any ‘arp or icmp’ Why arp is actually an issue? To connect two different pods across we use encapsulation building an overlay network with TUN TUN used encapsulate using a golang program TLDR internode pod communication here undelay stays untouched Consequences of L2 switching ARP spoofing ARP flooding, different IPs of pods Natural question, what if we can remove L2? Building a purely L3 routing Speed at which packets are arriving is not syncing with stuff hence packet loss Packet journey with VXLAN implementation From IP deterministically computer the MAC, the IP that you gave yourself?? Bridged pods+manual overlay -> Routed pods+manual overlay -> Routed pods+standard kernel overlay WIth BGP kubernetes coming very close to the hardware VXLAN is a known protocol not a lot of encapsulation is required Every solution removes a bottleneck but introduces a tradeoff Day - 02 Key Note 03 Valkey Valkey high performance key-value data store, sub millisecond access at any scale Caching is architecture not band aid, Cache first design Valkey removes redundant operations In LLM will save cost on semantically same queries https://valkey.io Key Note 04 IBM Geeta Gurnani Open source is powering AI acceleration but widening the Trust Gap Risk Atlas Nexus https://github.com/IBM/risk-atlas-nexus/ India specific governance framework - MANAV Scope of alignments: K R Varshney Key Note 05 Rust in OS Greg git.sr.ht/~gregkh/presentation-rust/ Search pragmatic engineer Linux CVE2024-43884 C scoped locks and allocations Test the bugs at compile time Rust can prevent a huge majority of security bugs at build time itself All data is evil, evaluate all types of data 80% of CVEs would be gone if code was written in Rust Rust is going to be the bedrock of android learn it Rust allows you to focus on logic more taking care of the trivial things Rust experiment is over now people are using it actively Make the compiler do the work More developers, less maintainers Valkey Chaos engineering for valkey via Valkey fuzzer demo Mostly a caching solution Standalone architecture or Cluster mode architecture, horizontal scaling Full mesh gossip protocol (TCP) Shard: Independent Primary -> Replica, design of Valkey Bugs: split brain- data divergence, stuck failover- partial outage, topology divergence- wrong ownership Chaos fuzzer+Agentic AI between dev and prod Our fuzzer will try to kill the primaries not the replicas Automated bug detection in this chaos demo Why it failed was quorum loss(not able to get the signal which one was primary which one replica) Multi AZ always on, 3+ shards minimum, force failover testing, connection pool drain, chaos Test https://github.com/valkey-io/valkey-fuzzer Dont trash it, Hack it Went in the wrong room Hey AI train Llama From slides, couldn’t understand shit Next evolution of Java @danieloh30 Why tuning required? Cold start, large images, memory consumption When a new container spawns up due to overload the java app takes time to start which in turn misinterprets kubernetes into unresponsive Kubernetes native java with quarkus Jib lean container images, also allows for fast updates since all the things are layered GraalVM native images Quarkus framework level optimizations Class data sharing for optimizing startup applications CRaC - coordinated response at checkpoint Running on JVM easy not on GraalVM Quarkus+Kubernetes is the way to go to put all tools together natively Refer images as well Bootloader on Android Google introduces GBL rust based Developed within AOSP Designed as UEFI Firmware stack -> UEFI Protocols(RNG. BlockIO) -> GBL(UEFI App) -> Android kernel Protocols required for GBL to work Cant understand shit she is just reading code from the slides Flattened image tree(FIT) currently used in uboot allows kernel images to be selected Boot control protocol? OTA? GBL mandates atleast two slots Boot memory protocol? Fastboot for custom transport layer Standard UEFI boot path GBL Android-16 onwards adopt GBL ESP partition has to be introduced, a mandate Now GBL can be debugged using trace32, but like rust it has different structures according to hardware Practical perfetto @stefan-lengfeld Perfetto, why tracing? allows you more dimension to inspect your code Tracing on android ui.perfetto.dev codeberg.org/lengfeld/perfetto-yocto-tutorial Check the above url for tutorial on perfetto In normal cases perfetto writes the configuration itself Perfetto advanced usages Built in histogram and stats Rectangle select, try that Android’s frame timeline -> every frame has additional information: jank type Android’s logcat support works only with userdebug builds Another server level tip is binder and flows Checkout release notes of perfetto for some awesome stuff Writing for machines Came late as I had to go for a short bathroom break Context gap Three different things: Rule files, Specs, Prompting pattern Agents.md should be cut to the chase First things to do after this session Couldn’t understand a lot of stuff but it was kind-of good to look into, refer pdf Agentic Delivery Refer from slides — End of Event —