Engineering Nerd retweeted
eBPF still gets filed under "observability tool". that undersells it badly
you load code straight into a running kernel, no reboot, no kernel module, no waiting three weeks for a patch window
Cloudflare uses it to kill DDoS traffic at the network card, before a packet ever reaches the kernel's normal network stack.
millions of packets a second, dropped for almost nothing
Meta built Katran, a load balancer that runs entirely inside the kernel.
no proxy hop, no extra trip through userspace
Cilium used the same trick to kill the sidecar.
service meshes used to mean bolting an Envoy container onto every single pod, burning CPU nobody asked for.
now the routing happens in-kernel and the sidecar is just gone
the one that actually got me: you can trace every syscall an AI agent makes, every file it opens, every socket it touches, without changing a line of its code.
it shows what the agent did on the machine, not what it printed to its own log
I spent years assuming the kernel was off-limits unless you were writing a driver. wrong assumption
a few dozen lines of eBPF and you're running your own code inside it, with a verifier making sure you can't crash the box
been down this hole far enough to build pktz: eBPF network monitor, per process, per connection, live - github.com/immanuwell/pktz
what's the strangest thing you've built with this?
Engineering Nerd retweeted
one CPU core can look at 28.8 million packets a second before the Linux kernel even wakes up
that's XDP. it runs inside the network driver, before the kernel allocates memory for the packet, before there's a socket, before there's a stack.
it just gets to decide: keep this, or throw it away, right now
most backend engineers never touch this layer in their whole career. I think that's a shame, because the stuff built on top of it is wild:
- Cloudflare drops over 10 million packets a second per core with it. they've auto-mitigated 100+ attacks above 2 billion packets per second, one of them a 3.8 Tbps flood, and nobody on their team touched a keyboard
- Meta's load balancer (Katran) runs this at every Facebook data center. the old way, iptables-style, tops out around a million packets a second per core and pins the CPU at 90% doing it. Katran does 10-100x that
- Cilium uses the same trick to rip iptables out of Kubernetes entirely. their own benchmarks show p99 network latency dropping from 45ms to under 2ms after the switch
all three are the same idea wearing different hats: read the packet before the OS does, decide fast, skip everything you don't need
I got curious enough to build a tiny version myself, an XDP load balancer with Maglev hashing, no shortcuts.
on one core of a normal laptop it pushed 9 million+ packets a second with zero drops. that's a laptop, not a data center NIC
code's here if you want to see how it works (written in Zig btw):
github.com/immanuwell/ringzeโฆ
what's the most "the kernel isn't supposed to be able to do that" thing you've seen eBPF pull off?
Engineering Nerd retweeted
Microcode in Intel's 8087 floating-point chip: the scale instruction
blog.adafruit.com/2026/09/14โฆ
best addition to this article: five robotics courses from Michigan University:
open-sourced, 100% FREE on GitHub, repo links below:
- ROB 101, computational linear algebra: the transforms and Jacobians from month 5, done properly
github.com/michiganrobotics/โฆ
- ROB 201, calculus for the modern engineer: full textbook sitting in the repo
github.com/michiganrobotics/โฆ
- ROB 311, how to build robots and make them move: the mechatronics half of months 2 and 3
github.com/michiganrobotics/โฆ
- ROB 501, mathematics for robotics: the rigorous version of everything in month 5
github.com/michiganrobotics/โฆ
- ROB 530, mobile robotics: Kalman filters, localization and SLAM, the theory under month 4's Nav2 stack
github.com/UMich-CURLY-teachโฆ
lecture videos on YouTube, homework, and code in MATLAB, Python, Julia and C++
start at 101, and leave 501 and 530 until you have actually built something, because both are graduate courses and will bounce you cold
this is the fundamentals layer underneath the article, not a replacement for it
five courses is not a degree either, so treat it as depth rather than the whole thing
build first, then come back and learn WHY it worked
Engineering Nerd retweeted
๐ Introducing DeepSeek-V4.1-Flash: smarter, faster, more efficient.
๐น Introducing the smallest model in our new architecture family, with native visual understanding.
๐น Designed for greater capability, faster inference, higher throughput, and scaling to larger models.
1/6
Engineering Nerd retweeted
One of THE fundamental ideas in Embedded Systems (and all of systems programming) is that of - Memory mapped I/O
What this means is some addresses as Memory and some addresses are configuration/status registers!!
What does this mean?
Given an address -
1. You can either use it to save data (memory) and that the data won't change (unless changed by the CPU). OR
2. Manipulations (writes/reads) from the address will manipulate an I/O (Peripheral. Like an LED lighting up or Byte being sent out on UART etc).
This simplifies the interactions of the CPU with the world outside it. The CPU can simply issue bus transactions (reads/writes) and achieve -
1. Execution of a program
2. Reading/Writing input/output
3. General computation by manipulating the memory (fetching values and saving results).
I cover this model in the second chapter of the book, available for online reading here - code.pyjamacafe.com/courses/โฆ
I will record a lecture and upload to the same page (and youtube) tomorrow.














