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Here are some notes on migrating a signed zone from BIND’s old auto-dnssec to its new dnssec-policy. I have been procrastinating this migration for years, and I avoided learning anything much about dnssec-policy until this month. I’m writing this from the perspective of a DNS operator rather than a BIND hacker. migrating from auto-dnssec risks to avoid things to know prior preparations which version test jig why I need a custom policy matching an existing policy more policy details max zone TTL DNSKEY TTL signature lifetimes other settings preparing the key files which key is which zone signing key key signing key deploy updated keys permissions change the big config change activate the change log messages query status key files DONE migrating from auto-dnssec My aim is to move my zones from old-style auto-dnssec to new-style dnssec-policy with minimal disruption. Specifically, I want to continue treating my DNSSEC keys as static configuration. I will port my existing keys over to...
9 months ago

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More from Tony Finch's blog

random numbers from pcg32 at 200 Gbit/s

One of the neat things about the PCG random number generator by Melissa O’Neill is its use of instruction-level parallelism: the PCG state update can run in parallel with its output permutation. However, PCG only has a limited amount of ILP, about 3 instructions. Its overall speed is limited by the rate at which a CPU can run a sequence where the output of one multiply-add feeds into the next multiply-add. … Or is it? With some linear algebra and some AVX512, I can generate random numbers from a single instance of pcg32 at 200 Gbit/s on a single core. This is the same sequence of random numbers generated in the same order as normal pcg32, but more than 4x faster. You can look at the benchmark in my pcg-dxsm repository. skip ahead the insight multipliers trying it out results skip ahead One of the slightly weird features that PCG gets from its underlying linear congruential generator is “seekability”: you can skip ahead k steps in the stream of random numbers in log(k) time. The PCG paper (in section 4.3.1) cites Forrest Brown’s paper, random numbers with arbitrary strides, which explains that the skip-ahead feature is useful for reproducibility of monte carlo simulations. But what caught my eye is the skip-ahead formula. Rephrased in programmer style, state[n+k] = state[n] * pow(MUL, k) + inc * (pow(MUL, k) - 1) / (MUL - 1) the insight The skip-ahead formula says that we can calculate a future state using a couple of multiplications. The skip-ahead multipliers depend only on the LCG multiplier, not on the variable state, nor on the configurable increment. That means that for a fixed skip ahead, we can precalculate the multipliers before compile time. The skip-ahead formula allows us to unroll the PCG data dependency chain. Normally, four iterations of the PCG state update look like, state0 = rng->state state1 = state0 * MUL + rng->inc state2 = state1 * MUL + rng->inc state3 = state2 * MUL + rng->inc state4 = state3 * MUL + rng->inc rng->state = state4 With the skip-ahead multipliers it looks like, state0 = rng->state state1 = state0 * MULs1 + rng->inc * MULi1 state2 = state0 * MULs2 + rng->inc * MULi2 state3 = state0 * MULs3 + rng->inc * MULi3 state4 = state0 * MULs4 + rng->inc * MULi4 rng->state = state4 These state calculations can be done in parallel using NEON or AVX vector instructions. The disadvantage is that calculating future states in parallel requires more multiplications than doing so in series, but that’s OK because modern CPUs have lots of ALUs. multipliers The skip-ahead formula is useful for jumping ahead long distances, because (as Forrest Brown explained) you can do the exponentiation in log(k) time using repeated squaring. (The same technique is used in for modexp in RSA.) But I’m only interested in the first few skip-ahead multipliers. I’ll define the linear congruential generator as: lcg(s, inc) = s * MUL + inc Which is used in PCG’s normal state update like: rng->state = lcg(rng->state, rng->inc) To precalculate the first few skip-ahead multipliers, we iterate the LCG starting from zero and one, like this: MULs0 = 1 MULs1 = lcg(MULs0, 0) MULs2 = lcg(MULs1, 0) MULi0 = 0 MULi1 = lcg(MULi0, 1) MULi2 = lcg(MULi1, 1) My benchmark code’s commentary includes a proof by induction, which I wrote to convince myself that these multipliers are correct. trying it out To explore how well this skip-ahead idea works, I have written a couple of variants of my pcg32_bytes() function, which simply iterates pcg32 and writes the results to a byte array. The variants have an adjustable amount of parallelism. One variant is written as scalar code in a loop that has been unrolled by hand a few times. I wanted to see if standard C gets a decent speedup, perhaps from autovectorization. The other variant uses the GNU C portable vector extensions to calculate pcg32 in an explicitly parallel manner. The benchmark also ensures the output from every variant matches the baseline pcg32_bytes(). results The output from the benchmark harness lists: the function variant either the baseline version or uN for a scalar loop unrolled N times or xN for vector code with N lanes its speed in bytes per nanosecond (aka gigabytes per second) its performance relative to the baseline There are small differences in style between the baseline and u1 functions, but their performance ought to be basically the same. Apple clang 16, Macbook Pro M1 Pro. This compiler is eager and fairly effective at autovectorizing. ARM NEON isn’t big enough to get a speedup from 8 lanes of parallelism. __ 3.66 bytes/ns x 1.00 u1 3.90 bytes/ns x 1.07 u2 6.40 bytes/ns x 1.75 u3 7.66 bytes/ns x 2.09 u4 8.52 bytes/ns x 2.33 x2 7.59 bytes/ns x 2.08 x4 10.49 bytes/ns x 2.87 x8 10.40 bytes/ns x 2.84 The following results were from my AMD Ryzen 9 7950X running Debian 12 “bookworm”, comparing gcc vs clang, and AVX2 vs AVX512. gcc is less keen to autovectorize so it doesn’t do very well with the unrolled loops. (Dunno why u1 is so much slower than the baseline.) gcc 12.2 -march=x86-64-v3 __ 5.57 bytes/ns x 1.00 u1 5.13 bytes/ns x 0.92 u2 5.03 bytes/ns x 0.90 u3 7.01 bytes/ns x 1.26 u4 6.83 bytes/ns x 1.23 x2 3.96 bytes/ns x 0.71 x4 8.00 bytes/ns x 1.44 x8 12.35 bytes/ns x 2.22 clang 16.0 -march=x86-64-v3 __ 4.89 bytes/ns x 1.00 u1 4.08 bytes/ns x 0.83 u2 8.76 bytes/ns x 1.79 u3 10.43 bytes/ns x 2.13 u4 10.81 bytes/ns x 2.21 x2 6.67 bytes/ns x 1.36 x4 12.67 bytes/ns x 2.59 x8 15.27 bytes/ns x 3.12 gcc 12.2 -march=x86-64-v4 __ 5.53 bytes/ns x 1.00 u1 5.53 bytes/ns x 1.00 u2 5.55 bytes/ns x 1.00 u3 6.99 bytes/ns x 1.26 u4 6.79 bytes/ns x 1.23 x2 4.75 bytes/ns x 0.86 x4 17.14 bytes/ns x 3.10 x8 20.90 bytes/ns x 3.78 clang 16.0 -march=x86-64-v4 __ 5.53 bytes/ns x 1.00 u1 4.25 bytes/ns x 0.77 u2 7.94 bytes/ns x 1.44 u3 9.31 bytes/ns x 1.68 u4 15.33 bytes/ns x 2.77 x2 9.07 bytes/ns x 1.64 x4 21.74 bytes/ns x 3.93 x8 26.34 bytes/ns x 4.76 That last result is pcg32 generating random numbers at 200 Gbit/s.

a week ago 9 votes
obfuscated C revisited

The International Obfuscated C Code Contest has a newly revamped web site, and the Judges have announced the 28th contest, to coincide with its 40th anniversary. (Or 41st?) The Judges have also updated the archive of past winners so that as many of them as possible work on modern systems. Accordingly, I took a look at my 1998 winner to see how much damage time hath wrought. When it is built, my program needs to go through the C preprocessor twice. There are a few reasons: It’s part of coercing the C compiler into compiling OFL, an obfuscated functional language. OFL has keywords l and b, short for let and be, so for example the function for constructing a pair is defined as l pair b (BB (B (B K)) C CI) In a less awful language that might be written let pair = λx λy λf λg (f x y) Anyway, the first pass of the C preprocessor turns a l (let) declaration into a macro #define pair b (BB (B (B K)) C CI) And the second pass expands the macros. (There’s a joke in the README that the OFL compiler has one optimization, function inlining (which is actually implemented by cpp macro expansion) but in fact inlining harms the performance of OFL.) The smaller the OFL interpreter, the more space there is for the program written in OFL. In the 1998 IOCCC rules, #define cost 7 characters, whereas l cost only one. I think the modern rules don’t count C or cpp keywords so there’s less reason to use this stupid trick to save space. Running the program through cpp twice is a horrible abuse of C and therefore just the kind of joke that the IOCCC encourages. (In fact the Makefile sends the program through cpp three times, twice explicitly and once as part of compiling to machine code. This is deliberately gratuitous, INABIAF.) There were a couple of ways this silliness caused problems. Modern headers are sensitive to which version of the C standard is in effect, wrt things like restrict keywords in standard library function declarations. The extra preprocessor invocations needed to be fixed to use consistent -std= options so that the final compilation doesn’t encounter language features from the future. Newer gcc emits #line directives around macro expansions. This caused problems for the declaration l ef E(EOF) which defines ef as a primitive value equal to EOF. After preprocessing this became #define ef E( #line 1213 "stdio.h" (-1) #line 69 "fanf.c" ) so the macro definition got truncated. The fix was to process the #include directives in the second preprocessor pass rather than the first. I vaguely remember some indecision when writing the program about whether to #include in the first or second pass, in particular whether preprocessing the headers twice would lead to trouble. First pass #include seemed to work and was shorter so that was what the original submission did. There’s one further change. The IOCCC Judges are trying to avoid compiler warnings about nonstandard arguments to main. To save a few characters, my entry had int main(int c) { ... } but the argument c isn’t used so I just removed it. The build commands still print “This may take some time to complete”, because in the 1990s if you tried to compile with optimization you would have been waiting a long time, if it completed at all. The revamped Makefile uses -O3, which takes gcc over 30 seconds and half a gigabyte of RAM. Quite a lot for less than 2.5 KiB of C!

a month ago 46 votes
nsnotifyd-2.3 released

D’oh, I lost track of a bug report that should have been fixed in nsnotifyd-2.2. Thus, hot on the heels of [the previous release][prev], here’s nsnotifyd-2.3. Sorry for causing extra work to my uncountably many users! The nsnotifyd daemon monitors a set of DNS zones and runs a command when any of them change. It listens for DNS NOTIFY messages so it can respond to changes promptly. It also uses each zone’s SOA refresh and retry parameters to poll for updates if nsnotifyd does not receive NOTIFY messages more frequently. It comes with a client program nsnotify for sending notify messages. This nsnotifyd-2.3 release includes some bug fixes: When nsnotifyd receives a SIGINT or SIGTERM while running the command, it failed to handle it correctly. Now it exits promptly. Many thanks to Athanasius for reporting the bug! Miscellaneous minor code cleanup and compiler warning suppression. Thanks also to Dan Langille who sent me a lovely appreciation: Now that I think of it, nsnotifyd is in my favorite group of software. That group is software I forget I’m running, because they just run and do the work. For years. I haven’t touched, modified, or configured nsnotifyd and it just keeps doing the job.

2 months ago 49 votes
nsnotifyd-2.2 released

I have made a new release of nsnotifyd, a tiny DNS server that just listens for NOTIFY messages and runs a script when one of your zones changes. This nsnotifyd-2.2 release includes a new feature: nsnotify can now send NOTIFY messages from a specific source address Thanks to Adam Augustine for the suggestion. I like receiving messages that say things like, Thanks for making this useful tool available for free.

2 months ago 50 votes
petnames and Zooko's fan

Recently the Spritely Institute published an introduction to Petnames, A humane approach to secure, decentralized naming. I have long been a fan of petnames, and graph naming systems in general. I first learned about them in the context of Mark Miller’s E programming language which was a distributed object capability system on the JVM. I gather Spritely are working on something similar, but with a more webby lisp/scheme flavour – and in fact Mark Miller is the second author on this article. An interesting thing that the article sort of hints at is that these kinds of systems have a fairly smooth range of operating points from decentralized petnames all the way to centralized globally unique names. Zooko’s triangle is actually more like a fan when the “human friendly” point is fixed: there’s an arc describing the tradeoffs, from personal through local to global. As a first step away from petnames, I like to use the term “nickname” for nearby instances of what they call “edge names” in the article: a nickname is not as personal as a petname, it’s a name you share with others. A bit further along the arc is the article’s example of the “bizdir” local business directory. It’s a trusted (and hopefully trustworthy) naming authority, but in a more local / federated fashion rather than centralized. How can a petname system function at the global+centralized point, so it could replace the DNS? It needs to pass the “billboard test”: I type in a name I see on a billboard and I get to the right place. (It might be a multipart name like a postal address or DNS name, with an extra “edge name” or two to provide enough disambiguating context.) I imagine that an operating system supplier might provide a few preconfigured petnames (it probably includes its own petname so the software can update itself securely), a lot like its preconfigured PKIX CA certificates. These petnames would refer to orgs like the “bizdir”, or Verisign, or Nominet, that act as nickname registries. Your collection of petnames is in effect your personal root zone, and the preconfigured petnames are in effect the default TLDs. When I was thinking about how a decentralized graph naming system could be made user-friendly and able to pass the billboard test I realised that there are organizational structures that are hard to avoid. I expect there would inevitably be something like the CA/Browser forum to mediate between OS suppliers and nickname registries: a petname ICANN. I wrote an older iteration of these ideas over a decade ago. Those notes suffer from too many DNS brainworms, but looking back, there’s some curious anti-DNS discussion. How might it be useful to be able to reach a name by multiple paths? Could you use that for attestation? What might it look like to have a shared context for names that is not global but is national or regional or local?

2 months ago 50 votes

More in programming

Diagnosis in engineering strategy.

Once you’ve written your strategy’s exploration, the next step is working on its diagnosis. Diagnosis is understanding the constraints and challenges your strategy needs to address. In particular, it’s about doing that understanding while slowing yourself down from deciding how to solve the problem at hand before you know the problem’s nuances and constraints. If you ever find yourself wanting to skip the diagnosis phase–let’s get to the solution already!–then maybe it’s worth acknowledging that every strategy that I’ve seen fail, did so due to a lazy or inaccurate diagnosis. It’s very challenging to fail with a proper diagnosis, and almost impossible to succeed without one. The topics this chapter will cover are: Why diagnosis is the foundation of effective strategy, on which effective policy depends. Conversely, how skipping the diagnosis phase consistently ruins strategies A step-by-step approach to diagnosing your strategy’s circumstances How to incorporate data into your diagnosis effectively, and where to focus on adding data Dealing with controversial elements of your diagnosis, such as pointing out that your own executive is one of the challenges to solve Why it’s more effective to view difficulties as part of the problem to be solved, rather than a blocking issue that prevents making forward progress The near impossibility of an effective diagnosis if you don’t bring humility and self-awareness to the process Into the details we go! This is an exploratory, draft chapter for a book on engineering strategy that I’m brainstorming in #eng-strategy-book. As such, some of the links go to other draft chapters, both published drafts and very early, unpublished drafts. Diagnosis is strategy’s foundation One of the challenges in evaluating strategy is that, after the fact, many effective strategies are so obvious that they’re pretty boring. Similarly, most ineffective strategies are so clearly flawed that their authors look lazy. That’s because, as a strategy is operated, the reality around it becomes clear. When you’re writing your strategy, you don’t know if you can convince your colleagues to adopt a new approach to specifying APIs, but a year later you know very definitively whether it’s possible. Building your strategy’s diagnosis is your attempt to correctly recognize the context that the strategy needs to solve before deciding on the policies to address that context. Done well, the subsequent steps of writing strategy often feel like an afterthought, which is why I think of diagnosis as strategy’s foundation. Where exploration was an evaluation-free activity, diagnosis is all about evaluation. How do teams feel today? Why did that project fail? Why did the last strategy go poorly? What will be the distractions to overcome to make this new strategy successful? That said, not all evaluation is equal. If you state your judgment directly, it’s easy to dispute. An effective diagnosis is hard to argue against, because it’s a web of interconnected observations, facts, and data. Even for folks who dislike your conclusions, the weight of evidence should be hard to shift. Strategy testing, explored in the Refinement section, takes advantage of the reality that it’s easier to diagnose by doing than by speculating. It proposes a recursive diagnosis process until you have real-world evidence that the strategy is working. How to develop your diagnosis Your strategy is almost certain to fail unless you start from an effective diagnosis, but how to build a diagnosis is often left unspecified. That’s because, for most folks, building the diagnosis is indeed a dark art: unspecified, undiscussion, and uncontrollable. I’ve been guilty of this as well, with The Engineering Executive’s Primer’s chapter on strategy staying silent on the details of how to diagnose for your strategy. So, yes, there is some truth to the idea that forming your diagnosis is an emergent, organic process rather than a structured, mechanical one. However, over time I’ve come to adopt a fairly structured approach: Braindump, starting from a blank sheet of paper, write down your best understanding of the circumstances that inform your current strategy. Then set that piece of paper aside for the moment. Summarize exploration on a new piece of paper, review the contents of your exploration. Pull in every piece of diagnosis from similar situations that resonates with you. This is true for both internal and external works! For each diagnosis, tag whether it fits perfectly, or needs to be adjusted for your current circumstances. Then, once again, set the piece of paper aside. Mine for distinct perspectives on yet another blank page, talking to different stakeholders and colleagues who you know are likely to disagree with your early thinking. Your goal is not to agree with this feedback. Instead, it’s to understand their view. The Crux by Richard Rumelt anchors diagnosis in this approach, emphasizing the importance of “testing, adjusting, and changing the frame, or point of view.” Synthesize views into one internally consistent perspective. Sometimes the different perspectives you’ve gathered don’t mesh well. They might well explicitly differ in what they believe the underlying problem is, as is typical in tension between platform and product engineering teams. The goal is to competently represent each of these perspectives in the diagnosis, even the ones you disagree with, so that later on you can evaluate your proposed approach against each of them. When synthesizing feedback goes poorly, it tends to fail in one of two ways. First, the author’s opinion shines through so strongly that it renders the author suspect. Your goal is never to agree with every team’s perspective, just as your diagnosis should typically avoid crowning any perspective as correct: a reader should generally be appraised of the details and unaware of the author. The second common issue is when a group tries to jointly own the synthesis, but create a fractured perspective rather than a unified one. I generally find that having one author who is accountable for representing all views works best to address both of these issues. Test drafts across perspectives. Once you’ve written your initial diagnosis, you want to sit down with the people who you expect to disagree most fervently. Iterate with them until they agree that you’ve accurately captured their perspective. It might be that they disagree with some other view points, but they should be able to agree that others hold those views. They might argue that the data you’ve included doesn’t capture their full reality, in which case you can caveat the data by saying that their team disagrees that it’s a comprehensive lens. Don’t worry about getting the details perfectly right in your initial diagnosis. You’re trying to get the right crumbs to feed into the next phase, strategy refinement. Allowing yourself to be directionally correct, rather than perfectly correct, makes it possible to cover a broad territory quickly. Getting caught up in perfecting details is an easy way to anchor yourself into one perspective prematurely. At this point, I hope you’re starting to predict how I’ll conclude any recipe for strategy creation: if these steps feel overly mechanical to you, adjust them to something that feels more natural and authentic. There’s no perfect way to understand complex problems. That said, if you feel uncertain, or are skeptical of your own track record, I do encourage you to start with the above approach as a launching point. Incorporating data into your diagnosis The strategy for Navigating Private Equity ownership’s diagnosis includes a number of details to help readers understand the status quo. For example the section on headcount growth explains headcount growth, how it compares to the prior year, and providing a mental model for readers to translate engineering headcount into engineering headcount costs: Our Engineering headcount costs have grown by 15% YoY this year, and 18% YoY the prior year. Headcount grew 7% and 9% respectively, with the difference between headcount and headcount costs explained by salary band adjustments (4%), a focus on hiring senior roles (3%), and increased hiring in higher cost geographic regions (1%). If everyone evaluating a strategy shares the same foundational data, then evaluating the strategy becomes vastly simpler. Data is also your mechanism for supporting or critiquing the various views that you’ve gathered when drafting your diagnosis; to an impartial reader, data will speak louder than passion. If you’re confident that a perspective is true, then include a data narrative that supports it. If you believe another perspective is overstated, then include data that the reader will require to come to the same conclusion. Do your best to include data analysis with a link out to the full data, rather than requiring readers to interpret the data themselves while they are reading. As your strategy document travels further, there will be inevitable requests for different cuts of data to help readers understand your thinking, and this is somewhat preventable by linking to your original sources. If much of the data you want doesn’t exist today, that’s a fairly common scenario for strategy work: if the data to make the decision easy already existed, you probably would have already made a decision rather than needing to run a structured thinking process. The next chapter on refining strategy covers a number of tools that are useful for building confidence in low-data environments. Whisper the controversial parts At one time, the company I worked at rolled out a bar raiser program styled after Amazon’s, where there was an interviewer from outside the team that had to approve every hire. I spent some time arguing against adding this additional step as I didn’t understand what we were solving for, and I was surprised at how disinterested management was about knowing if the new process actually improved outcomes. What I didn’t realize until much later was that most of the senior leadership distrusted one of their peers, and had rolled out the bar raiser program solely to create a mechanism to control that manager’s hiring bar when the CTO was disinterested holding that leader accountable. (I also learned that these leaders didn’t care much about implementing this policy, resulting in bar raiser rejections being frequently ignored, but that’s a discussion for the Operations for strategy chapter.) This is a good example of a strategy that does make sense with the full diagnosis, but makes little sense without it, and where stating part of the diagnosis out loud is nearly impossible. Even senior leaders are not generally allowed to write a document that says, “The Director of Product Engineering is a bad hiring manager.” When you’re writing a strategy, you’ll often find yourself trying to choose between two awkward options: Say something awkward or uncomfortable about your company or someone working within it Omit a critical piece of your diagnosis that’s necessary to understand the wider thinking Whenever you encounter this sort of debate, my advice is to find a way to include the diagnosis, but to reframe it into a palatable statement that avoids casting blame too narrowly. I think it’s helpful to discuss a few concrete examples of this, starting with the strategy for navigating private equity, whose diagnosis includes: Based on general practice, it seems likely that our new Private Equity ownership will expect us to reduce R&D headcount costs through a reduction. However, we don’t have any concrete details to make a structured decision on this, and our approach would vary significantly depending on the size of the reduction. There are many things the authors of this strategy likely feel about their state of reality. First, they are probably upset about the fact that their new private equity ownership is likely to eliminate colleagues. Second, they are likely upset that there is no clear plan around what they need to do, so they are stuck preparing for a wide range of potential outcomes. However they feel, they don’t say any of that, they stick to precise, factual statements. For a second example, we can look to the Uber service migration strategy: Within infrastructure engineering, there is a team of four engineers responsible for service provisioning today. While our organization is growing at a similar rate as product engineering, none of that additional headcount is being allocated directly to the team working on service provisioning. We do not anticipate this changing. The team didn’t agree that their headcount should not be growing, but it was the reality they were operating in. They acknowledged their reality as a factual statement, without any additional commentary about that statement. In both of these examples, they found a professional, non-judgmental way to acknowledge the circumstances they were solving. The authors would have preferred that the leaders behind those decisions take explicit accountability for them, but it would have undermined the strategy work had they attempted to do it within their strategy writeup. Excluding critical parts of your diagnosis makes your strategies particularly hard to evaluate, copy or recreate. Find a way to say things politely to make the strategy effective. As always, strategies are much more about realities than ideals. Reframe blockers as part of diagnosis When I work on strategy with early-career leaders, an idea that comes up a lot is that an identified problem means that strategy is not possible. For example, they might argue that doing strategy work is impossible at their current company because the executive team changes their mind too often. That core insight is almost certainly true, but it’s much more powerful to reframe that as a diagnosis: if we don’t find a way to show concrete progress quickly, and use that to excite the executive team, our strategy is likely to fail. This transforms the thing preventing your strategy into a condition your strategy needs to address. Whenever you run into a reason why your strategy seems unlikely to work, or why strategy overall seems difficult, you’ve found an important piece of your diagnosis to include. There are never reasons why strategy simply cannot succeed, only diagnoses you’ve failed to recognize. For example, we knew in our work on Uber’s service provisioning strategy that we weren’t getting more headcount for the team, the product engineering team was going to continue growing rapidly, and that engineering leadership was unwilling to constrain how product engineering worked. Rather than preventing us from implementing a strategy, those components clarified what sort of approach could actually succeed. The role of self-awareness Every problem of today is partially rooted in the decisions of yesterday. If you’ve been with your organization for any duration at all, this means that you are directly or indirectly responsible for a portion of the problems that your diagnosis ought to recognize. This means that recognizing the impact of your prior actions in your diagnosis is a powerful demonstration of self-awareness. It also suggests that your next strategy’s success is rooted in your self-awareness about your prior choices. Don’t be afraid to recognize the failures in your past work. While changing your mind without new data is a sign of chaotic leadership, changing your mind with new data is a sign of thoughtful leadership. Summary Because diagnosis is the foundation of effective strategy, I’ve always found it the most intimidating phase of strategy work. While I think that’s a somewhat unavoidable reality, my hope is that this chapter has somewhat prepared you for that challenge. The four most important things to remember are simply: form your diagnosis before deciding how to solve it, try especially hard to capture perspectives you initially disagree with, supplement intuition with data where you can, and accept that sometimes you’re missing the data you need to fully understand. The last piece in particular, is why many good strategies never get shared, and the topic we’ll address in the next chapter on strategy refinement.

10 hours ago 3 votes
My friend, JT

I’ve had a cat for almost a third of my life.

2 hours ago 2 votes
[Course Launch] Hands-on Introduction to X86 Assembly

A Live, Interactive Course for Systems Engineers

4 hours ago 2 votes
It’s cool to care

I’m sitting in a small coffee shop in Brooklyn. I have a warm drink, and it’s just started to snow outside. I’m visiting New York to see Operation Mincemeat on Broadway – I was at the dress rehearsal yesterday, and I’ll be at the opening preview tonight. I’ve seen this show more times than I care to count, and I hope US theater-goers love it as much as Brits. The people who make the show will tell you that it’s about a bunch of misfits who thought they could do something ridiculous, who had the audacity to believe in something unlikely. That’s certainly one way to see it. The musical tells the true story of a group of British spies who tried to fool Hitler with a dead body, fake papers, and an outrageous plan that could easily have failed. Decades later, the show’s creators would mirror that same spirit of unlikely ambition. Four friends, armed with their creativity, determination, and a wardrobe full of hats, created a new musical in a small London theatre. And after a series of transfers, they’re about to open the show under the bright lights of Broadway. But when I watch the show, I see a story about friendship. It’s about how we need our friends to help us, to inspire us, to push us to be the best versions of ourselves. I see the swaggering leader who needs a team to help him truly achieve. The nervous scientist who stands up for himself with the support of his friends. The enthusiastic secretary who learns wisdom and resilience from her elder. And so, I suppose, it’s fitting that I’m not in New York on my own. I’m here with friends – dozens of wonderful people who I met through this ridiculous show. At first, I was just an audience member. I sat in my seat, I watched the show, and I laughed and cried with equal measure. After the show, I waited at stage door to thank the cast. Then I came to see the show a second time. And a third. And a fourth. After a few trips, I started to see familiar faces waiting with me at stage door. So before the cast came out, we started chatting. Those conversations became a Twitter community, then a Discord, then a WhatsApp. We swapped fan art, merch, and stories of our favourite moments. We went to other shows together, and we hung out outside the theatre. I spent New Year’s Eve with a few of these friends, sitting on somebody’s floor and laughing about a bowl of limes like it was the funniest thing in the world. And now we’re together in New York. Meeting this kind, funny, and creative group of people might seem as unlikely as the premise of Mincemeat itself. But I believed it was possible, and here we are. I feel so lucky to have met these people, to take this ridiculous trip, to share these precious days with them. I know what a privilege this is – the time, the money, the ability to say let’s do this and make it happen. How many people can gather a dozen friends for even a single evening, let alone a trip halfway round the world? You might think it’s silly to travel this far for a theatre show, especially one we’ve seen plenty of times in London. Some people would never see the same show twice, and most of us are comfortably into double or triple-figures. Whenever somebody asks why, I don’t have a good answer. Because it’s fun? Because it’s moving? Because I enjoy it? I feel the need to justify it, as if there’s some logical reason that will make all of this okay. But maybe I don’t have to. Maybe joy doesn’t need justification. A theatre show doesn’t happen without people who care. Neither does a friendship. So much of our culture tells us that it’s not cool to care. It’s better to be detached, dismissive, disinterested. Enthusiasm is cringe. Sincerity is weakness. I’ve certainly felt that pressure – the urge to play it cool, to pretend I’m above it all. To act as if I only enjoy something a “normal” amount. Well, fuck that. I don’t know where the drive to be detached comes from. Maybe it’s to protect ourselves, a way to guard against disappointment. Maybe it’s to seem sophisticated, as if having passions makes us childish or less mature. Or perhaps it’s about control – if we stay detached, we never have to depend on others, we never have to trust in something bigger than ourselves. Being detached means you can’t get hurt – but you’ll also miss out on so much joy. I’m a big fan of being a big fan of things. So many of the best things in my life have come from caring, from letting myself be involved, from finding people who are a big fan of the same things as me. If I pretended not to care, I wouldn’t have any of that. Caring – deeply, foolishly, vulnerably – is how I connect with people. My friends and I care about this show, we care about each other, and we care about our joy. That care and love for each other is what brought us together, and without it we wouldn’t be here in this city. I know this is a once-in-a-lifetime trip. So many stars had to align – for us to meet, for the show we love to be successful, for us to be able to travel together. But if we didn’t care, none of those stars would have aligned. I know so many other friends who would have loved to be here but can’t be, for all kinds of reasons. Their absence isn’t for lack of caring, and they want the show to do well whether or not they’re here. I know they care, and that’s the important thing. To butcher Tennyson: I think it’s better to care about something you cannot affect, than to care about nothing at all. In a world that’s full of cynicism and spite and hatred, I feel that now more than ever. I’d recommend you go to the show if you haven’t already, but that’s not really the point of this post. Maybe you’ve already seen Operation Mincemeat, and it wasn’t for you. Maybe you’re not a theatre kid. Maybe you aren’t into musicals, or history, or war stories. That’s okay. I don’t mind if you care about different things to me. (Imagine how boring the world would be if we all cared about the same things!) But I want you to care about something. I want you to find it, find people who care about it too, and hold on to them. Because right now, in this city, with these people, at this show? I’m so glad I did. And I hope you find that sort of happiness too. Some of the people who made this trip special. Photo by Chloe, and taken from her Twitter. Timing note: I wrote this on February 15th, but I delayed posting it because I didn’t want to highlight the fact I was away from home. [If the formatting of this post looks odd in your feed reader, visit the original article]

yesterday 3 votes
Stick with the customer

One of the biggest mistakes that new startup founders make is trying to get away from the customer-facing roles too early. Whether it's customer support or it's sales, it's an incredible advantage to have the founders doing that work directly, and for much longer than they find comfortable. The absolute worst thing you can do is hire a sales person or a customer service agent too early. You'll miss all the golden nuggets that customers throw at you for free when they're rejecting your pitch or complaining about the product. Seeing these reasons paraphrased or summarized destroy all the nutrients in their insights. You want that whole-grain feedback straight from the customers' mouth!  When we launched Basecamp in 2004, Jason was doing all the customer service himself. And he kept doing it like that for three years!! By the time we hired our first customer service agent, Jason was doing 150 emails/day. The business was doing millions of dollars in ARR. And Basecamp got infinitely, better both as a market proposition and as a product, because Jason could funnel all that feedback into decisions and positioning. For a long time after that, we did "Everyone on Support". Frequently rotating programmers, designers, and founders through a day of answering emails directly to customers. The dividends of doing this were almost as high as having Jason run it all in the early years. We fixed an incredible number of minor niggles and annoying bugs because programmers found it easier to solve the problem than to apologize for why it was there. It's not easy doing this! Customers often offer their valuable insights wrapped in rude language, unreasonable demands, and bad suggestions. That's why many founders quit the business of dealing with them at the first opportunity. That's why few companies ever do "Everyone On Support". That's why there's such eagerness to reduce support to an AI-only interaction. But quitting dealing with customers early, not just in support but also in sales, is an incredible handicap for any startup. You don't have to do everything that every customer demands of you, but you should certainly listen to them. And you can't listen well if the sound is being muffled by early layers of indirection.

yesterday 4 votes