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This is Part 1 of a 5-part series on Cisco EEM. We start here with the fundamentals and a few working applets, then build toward self-healing networks, automated diagnostics, compliance guardrails, and a complete real-world deployment. Ask ten network engineers what they use EEM for, and nine will say the same thing: "Oh, I have an applet that auto-recovers err-disabled ports." Then they never touch it again. That's a shame, because Embedded Event Manager is one of the most capable tools already sitting inside every Cisco IOS device you own — and almost nobody uses more than 1% of it. It's a full automation engine that lives on the box . No external server. No API gateway. No orchestration platform. Just the router or switch, watching itself, ready to react the instant something happens — even if the WAN is cut, the NMS is down, and it's three in the morning. This series is about using that other 99%. By the end you'll have a toolkit of applets you can deploy and, more importantly, a way of thinking about on-box automation. But we start at the foundation: what EEM actually is, how it's put together, and why "on the box" is a bigger deal than it sounds. What EEM actually is Embedded Event Manager is an event-driven automation framework built into Cisco IOS, IOS-XE, and NX-OS. Strip away the jargon and it's a very simple idea: When something happens, do something about it — automatically, on the device itself. That "something happens" is an event . That "do something" is one or more actions . Bundle an event with its actions and you have an applet — the basic unit of EEM. That's the whole model. The power comes from how many different things can be an event , and how much an action can do. Events EEM can watch for include: A syslog message matching a pattern (an interface flapping, an HSRP state change, a config being saved). An SNMP OID crossing a threshold (CPU over 85%, a power supply going absent, temperature rising). A CLI command being entered (someone typing wr

New climate evidence adds context to these long voyages.

Researchers cobbled together funding and time to show how quantum computing could aid in the development of drugs to help underserved populations and combat rare diseases.

series: Prediksi Survival Klub Debutan Kenapa Project Ini? Setiap musim, klub yang promosi ke liga top (Premier League, La Liga, dst.) menghadapi risiko besar: sekitar 2 dari 3 klub yang naik biasanya kembali terdegradasi di musim pertama mereka. Saya penasaran — bisakah performa di beberapa laga awal musim memberi sinyal dini soal peluang klub tersebut bertahan? Ini jadi project portofolio pertama saya sebagai data scientist yang baru mulai (0-1 tahun pengalaman). Saya sengaja pilih topik yang saya suka (sepak bola) supaya prosesnya tetap enjoyable, bukan cuma "tutorial project" generik. Rencana Project Pertanyaan utama: Berdasarkan performa 8 laga pertama musim debut, seberapa besar peluang klub promosi bertahan hingga musim berikutnya (tidak degradasi)? Data yang dipakai: football-data.co.uk — data hasil pertandingan tiap musim sejak 1993/1994 Wikipedia (halaman musim liga) — daftar klub promosi & klasemen akhir musim Tech stack: pandas , requests untuk data collection scikit-learn untuk modeling (mulai dari Logistic Regression sebagai baseline) imbalanced-learn untuk handle class imbalance Streamlit + Plotly untuk dashboard interaktif Deploy ke Streamlit Community Cloud Timeline (Build in Public) Saya bikin timeline ini publik supaya ada tekanan yang sehat untuk benar-benar menyelesaikannya, bukan cuma jadi ide yang menguap: Checkpoint Target Tanggal Yang Harus Selesai Part 1 (post ini) 11 Juli 2026 Kickoff, rencana, environment siap Part 2 15 Juli 2026 Dataset jadi, push ke GitHub Part 3 17 Juli 2026 EDA selesai, insight awal Part 4 24 Juli 2026 Model final dipilih + evaluasi Part 5 31 Juli 2026 Dashboard live di Streamlit Cloud Part 6 (final) 8 Agustus 2026 Project selesai, recap lengkap Tantangan yang Sudah Saya Antisipasi Data leakage — fitur harus dihitung dari laga awal musim saja, bukan seluruh musim, biar model beneran memprediksi bukan "menyontek" hasil akhir Dataset kecil — kemungkinan hanya ~60-100 sampel klub, jadi saya mulai dari model sederhana (Lo

I’m currently relearning the Core Three — HTML, CSS, and JavaScript — as I work toward becoming a full-stack JavaScript developer. Before I came back to learning software, I spent 22 years working industrial turnarounds. One lesson from that world has followed me into software engineering: Never trust a single point of failure. In industrial maintenance, there’s a safety practice called double block-and-bleed . Instead of trusting one isolation valve, you use two independent valves with a bleed point between them. If one valve leaks, you know immediately. The entire system assumes individual components can fail. Safety doesn’t come from perfect parts. It comes from independent layers of protection. That idea completely changed how I think about CI pipelines. When I first started relearning web development, my mindset was simple: Run Lighthouse. Everything green? Great. 100 across the board locally? Even better. Ship it. Different results after deployment? Uh-oh. Now I see Lighthouse as one checkpoint — not the finish line. A fast website can still have accessibility issues. An accessible site can still have broken metadata. Good SEO won’t catch rendering bugs. Passing unit tests won’t tell you if the generated HTML is malformed. Every tool has blind spots. No single tool should get the final vote. So instead of asking: “Did my tests pass?” I ask: “What kinds of failures could still slip through?” That question naturally leads to layered validation. Formatting Linting Type checking Accessibility checks Performance audits HTML validation SEO analysis Manual review None of these tools is perfect. Together, they’re much stronger than any one of them alone. The more I learn about software, the more I find myself applying lessons from heavy industry. Different environment. Different risks. The same engineering mindset. Assume components will fail. Design systems that fail safely. That’s becoming the philosophy behind every test matrix and CI pipeline I’m designing. What’s

Harvard astrophysicist Avi Loeb will head the UAP Science Advisory Council established by the White House, the Pentagon, the Office of the Director of National Intelligence, the FBI, and "the intelligence community." The Council will provide scientific reports and advice to the UAP Governing Board, in an effort to "resolve the nature of UAP," or […]

It's unclear how the planet avoided its star's bloated red giant stage.

For the first time since 1995, the Bureau of Land Management is rewriting its grazing regulations.

The probe sent back the first pictures of the asteroid Kamo’oalewa. Next step: landing on the surface and collecting samples to send back to Earth.

As the climate phenomenon sends warm water surging across the eastern Pacific, some parts of the fishing industry are suffering—but other regions are seeing a windfall.

Adapted from an appendix of my MS thesis. Markov Chain Monte Carlo Almost as soon as computers were invented, they were used for simulation. Markov chain Monte Carlo (MCMC) was invested as Los Alamos, Metropolis et al (1953) simulated a liquid in equilibrium with its gas phase. Their tour de force was the realization that they did not need to simulate the exact dynamics, they only needed to simulate some Markov chain with the same equilibrium distribution. The Metropolis algorithm was widely used by chemists and physicists, but was not widely known among statisticians until after 1990. Hastings (1970) generalized the Metropolis algorithm, and simulations following his scheme are said to use the Metropolis-Hastings (MH) algorithm [1]. A special case of the MH algorithm was introduced by Geman et al (1984) discussing optimization to find the posterior mode rather than simulation. Algorithms following their scheme are said to use the Gibbs sampler. It took some time for the spatial statistics community to understand that the Gibbs sampler simulated the posterior distribution, thus enabling full Bayesian inference of all kinds. Gelfand et al (1990) made the wider Bayesian community aware of the Gibbs sampler, and then it was rapidly realized that most Bayesian inference could be done using MCMC, whereas very little could be done without MCMC. Green (1995) generalized the MH algorithm as much as it could be generalized [1]. Theoretical Foundations A sequence X 1 ​ , X 2 ​ , … of random elements of some set is a Markov chain if the conditional distribution of X n + 1 ​ given X 1 ​ , … , X n ​ depends on X n ​ only. The set in which the X i ​ take values is called the state space of the Markov chain. A Markov chain has stationary transition probabilities if the conditional distribution of X n + 1 ​ given X n ​ does not depend on n . This is the main kind of Markov chain of interest in MCMC. The joint distribution of a Markov chain is determined by the following [1]. The ma

Johannes Heidecke’s departure comes as OpenAI tries to further integrate its research and safety teams.

CISA said it "missed" an opportunity to get ahead of the security incident by not creating a response plan ahead of time.

Pull a hot steel bolt out of a furnace and quench it in oil, and a fair question is: does the bolt cool from the outside in, with a sharp temperature difference between its skin and its core, or does the whole thing drop in temperature more or less together? The answer is not obvious from the part itself. A thin copper washer and a thick ceramic block behave very differently in the same bath, even at the same starting temperature. The Biot number is the small calculation that settles this question before you commit to any heavy analysis. It tells you, in a single dimensionless figure, whether an object can be treated as having one uniform temperature or whether you must resolve a temperature gradient inside it. That distinction changes the math from a one-line exponential decay to a partial differential equation. Why this calculation matters Transient heating and cooling problems show up everywhere: heat-treating metal parts, quenching forgings, cooling electronics, baking or chilling food, warming up an engine block. In every one of these, the engineer wants to know how the temperature changes over time. The hard version of that question requires solving the heat conduction equation across the body, with position and time as variables. The easy version is the lumped-capacitance model, which treats the whole object as a single point at one temperature. It reduces the problem to a simple first-order exponential. The catch is that the lumped model is only valid when internal conduction is fast compared with surface convection. The Biot number is exactly the check that tells you whether that condition holds. Skip the check and apply the lumped model where it does not belong, and you can badly mispredict cooling times, residual stresses, and the risk of cracking from thermal gradients. The core formula The Biot number compares two thermal resistances. One is the resistance to conducting heat through the inside of the solid. The other is the resistance to carrying heat a

Notes from bringing up a PCIe WiFi module on i.MX8MQ; symptoms and how to diagnose them. Phy link never came up — what does this mean? This message is typically seen in dmesg as shown below. [ 3.828121] imx6q-pcie 33800000.pcie: iATU: unroll T, 4 ob, 4 ib, align 64K, limit 4G [ 4.807241] imx6q-pcie 33c00000.pcie: Phy link never came up [ 4.841482] imx6q-pcie 33800000.pcie: Phy link never came up [ 5.821279] imx6q-pcie 33c00000.pcie: Phy link never came up [ 5.830481] imx6q-pcie 33c00000.pcie: PCI host bridge to bus 0001:00 [ 5.854997] imx6q-pcie 33800000.pcie: Phy link never came up [ 5.862205] imx6q-pcie 33800000.pcie: PCI host bridge to bus 0000:00 It means one of the following The PCIe peripheral is not powered up. PCIe reset is not deasserted, so the chip is in reset. This could be because the DTB is deasserting an incorrect GPIO. Reference clock is not enabled Using the incorrect PCIe controller in the device tree. As we can see that both the PCIe controllers can report this. So first determine which controller is the peripheral hooked to. More on this in the next section. Which PCIe controller is my device on? ( &pcie0 vs &pcie1 ) The rule here is to match by address and not by label/name. If the schematic calls out controllers as PCIE1 and PCIE2, and the device tree lists pcie0 and pcie1, understand the mapping. The DTS label is arbitrary - match by register base ( @address in the node name), which is the same in the DTS reg and the reference memory map. For definitive addresses look in the .dtsi , as sometimes the manuals are misleading. Given below is a mapping table for i.MX8MQ DTS. | ADDRESS (in .dtsi) | Silicon (RM) Label &pcie0 | 0x33800000 | PCIe1 &pcie1 | 0x33c00000 | PCIe2 The addresses are listed in the chip’s memory layout are from processor reference manual. Below is snapshot from the i.MX8MQ reference manual, where the layout for the core A-53 is listed. Start Address | End Address | Size | Description 3381_0000 | 3381_3FFF | 4MB | PCIe-2 << inco

Meta told Dylan Byers, of Puck News, that it had nixed the feature after backlash from its user base.

Data centers are driving up the company’s use of electricity—and carbon pollution.

Reinforcement learning uses error information to adjust control algorithms.

Schneider, who formerly served as the CEO of Automattic and is a partner at True Ventures, says he is "all in" on the unconventional social media platform.

The iPhone maker claims OpenAI encouraged poached employees to bring over confidential presentations, secret prototypes, and key supplier details.