Back in 2023 I went through an inter-state move with Flat Rate Movers, LTD., a.k.a. Flat Rate Moving, US DOT 488466, MC 254356. They bungled the job, damaged my stuff and my house, and then refused to talk to me or their own insurance company. I placed dozens of calls, wrote letters, emails, nothing worked. I finally got some money for the damage by working with their insurer directly. I know a lot of folks struggle with moving companies, and figured I’d write up a bit of this story in case it helps. You can skip ahead if you just want to know about the FMCSA insurer database.
In November 2022 I scheduled the move with Flat Rate. We took a detailed video inventory of everything to be moved. I paid over $14,000 for an “all inclusive move”, which, per the move plan, included full packing services, blankets for furniture, boxes for electronics, lamps, and bicycles, a custom crate for a tabletop, and “extended valuation coverage”—their insurance policy.
Submitted to the Department of State, which is requesting comments on a proposed change which would align US passport gender markers with executive order 14168.
Executive order 14168 is biologically incoherent and socially cruel. All passport applicants should be allowed to select whatever gender markers they feel best fit, including M, F, or X.
A few quick notes for other folks who are geoblocking the UK. I just set up a basic geoblock with Nginx on Debian. This is all stuff you can piece together, but the Maxmind and Nginx docs are a little vague about the details, so I figure it’s worth an actual writeup. My Nginx expertise is ~15 years out of date, so this might not be The Best Way to do things. YMMV.
First, register for a free MaxMind account; you’ll need this to subscribe to their GeoIP database. Then set up a daemon to maintain a copy of the lookup file locally, and Nginx’s GeoIP2 module:
This is not at all news, but it comes up often enough that I think there should be a concise explanation of the problem. People, myself included, like to say that POSIX time, also known as Unix time, is the number of seconds since the Unix epoch, which was 1970-01-01 at 00:00:00.
This is not true. Or rather, it isn’t true in the sense most people think. For example, it is presently 2024-12-25 at 18:51:26 UTC. The POSIX time is 1735152686. It has been 1735152713 seconds since the POSIX epoch. The POSIX time number is twenty-seven seconds lower.
In June 2023, when Threads announced their plans to federate with other Fediverse instances, there was a good deal of debate around whether smaller instances should allow federation or block it pre-emptively. As one of the admins of woof.group, I wrote about some of the potential risks and rewards of federating with Threads. We decided to wait and see.
In my queer and leather circles, Facebook and Instagram have been generally understood as hostile environments for over a decade. In 2014, their “Real Name” policy made life particularly difficult for trans people, drag queens, sex workers, and people who, for various reasons, needed to keep their real name disconnected from their queer life. My friends have been repeatedly suspended from both platforms for showing too much skin, or using the peach emoji. Meta’s moderation has been aggressive, opaque, and wildly inconsistent: sometimes full nudity is fine; other times a kiss or swimsuit is beyond the line. In some circles, maintaining a series of backup accounts in advance of one’s ban became de rigueur.
I’m in the process of replacing a old radiator system with a centrally-ducted, air-source heat pump system with electric resistive backup heat. I’ve found that the default ecobee algorithm seems to behave surprisingly poorly for this system, and wanted to write up some of the settings that I’ve found yield better behavior.
A disclaimer. I’m not an HVAC professional. I have two decades in software operations, a background in physics, and far too much experience inferring system dynamics from timeseries graphs. This advice may void your warranty, burn your house down, etc.; everything you do is at your own risk.
Recently I’ve had users of my libraries start reporting mysterious errors due to a missing reference to SequencedCollection, a Java interface added in JDK 21:
Execution error (ClassNotFoundException) at
jdk.internal.loader.BuiltinClassLoader/loadClass (BuiltinClassLoader.java:641).
java.util.SequencedCollection
This story is not practical advice. For me, it’s closing the book on an almost two-year saga. For you, I hope it’s an enjoyable bit of bureaucratic schadenfreude. For Anthem, I hope it’s the subject of a series of painful but transformative meetings. This is not an isolated event. I’ve had dozens of struggles with Anthem customer support, and they all go like this.
If you’re looking for practical advice: it’s this. Be polite. Document everything. Keep a log. Follow the claims process. Check the laws regarding insurance claims in your state. If you pass the legally-mandated deadline for your claim, call customer service. Do not allow them to waste a year of your life, or force you to resubmit your claim from scratch. Initiate a complaint with your state regulators, and escalate directly to Gail Boudreaux’s team–or whoever Anthem’s current CEO is.
People keep asking why Jepsen is written in Clojure, so I figure it’s worth having a referencable answer. I’ve programmed in something like twenty languages. Why choose a Weird Lisp?
Jepsen is built for testing concurrent systems–mostly databases. Because it tests concurrent systems, the language itself needs good support for concurrency. Clojure’s immutable, persistent data structures make it easier to write correct concurrent programs, and the language and runtime have excellent concurrency support: real threads, promises, futures, atoms, locks, queues, cyclic barriers, all of java.util.concurrent, etc. I also considered languages (like Haskell) with more rigorous control over side effects, but decided that Clojure’s less-dogmatic approach was preferable.
One of the things we struggle with on woof.group is un-actionable reports. For various reasons, most of the reports we handle are for posts that are either appropriately content-warned or don’t require a content warning under our content policy–things like faces, butts, and shirtlessness. We can choose to ignore reports from a domain, but we’d rather not do that: it means we might miss out on important reports that require moderator action. We can also talk to remote instance administrators and ask them to talk to their users about not sending copies of reports to the remote instance if they don’t know what the remote instance policy is, but that’s time consuming, and we only want to do it if there’s an ongoing problem.
I finally broke down and dug around in the data model to figure out how to get statistics on this. If you’re a Mastodon admin and you’d like to figure out which domains send you the most non-actionable reports, you can run this at rails console:
Jepsen is a library for writing tests of concurrent systems: everything from single-node data structures to distributed databases and queues. A key part of this process is recording a history of operations performed during the test. Jepsen checkers analyze a history to find consistency anomalies and to compute performance metrics. Traditionally Jepsen has stored the history in a Clojure vector (an immutable in-memory data structure like an array), and serialized it to disk at the end of the test. This limited Jepsen to histories on the order of tens of millions of operations. It also meant that if Jepsen crashed during a several-hour test run, it was impossible to recover any of the history for analysis. Finally, saving and loading large tests involved long wait times—sometimes upwards of ten minutes.
Over the last year I’ve been working on ways to resolve these problems. Generators are up to ten times faster. A new operation datatype makes each operation smaller and faster to access. Jepsen’s new on-disk format allows us to stream histories incrementally to disk, to work with histories of up to a billion operations far exceeding available memory, to recover safely from crashes, and to load tests almost instantly by deserializing data lazily. New history datatypes support both densely and sparsely indexed histories, and efficiently cache auxiliary indices. They also support lazy disk-backed map and filter. These histories support both linear and concurrent folds, which dramatically improves checker performance on multicore systems: real-world checkers can readily analyze 250,000 operations/sec. Histories support multi-query optimization: when multiple threads fold over the same history, a query planner automatically fuses those folds together to perform them in a single pass. Since Jepsen often folds dozens of times over the same history, this saves a good deal of disk IO and deserialization time. These features are enabled by a new, transactional, dependency-aware task executor.
Again with the reductions! I keep writing code which reduces over a collection, keeping track of more than one variable. For instance, here’s one way to find the mean of a collection of integers:
(defn mean
"A reducer to find the mean of a collection. Accumulators are [sum count] pairs."
([] [0 0])
([[sum count]] (/ sum count))
([[sum count] x]
[(+ sum x) (inc count)]))
This mean function is what Clojure calls a reducer, or a reducing function. With no arguments, it constructs a fresh accumulator. With two arguments, it combines an element of some collection with the accumulator, returning a new accumulator. With one argument, it transforms the accumulator into some final result.