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42,014,686 | kateanderson | 2024-11-01T07:13:01 | The On-Premise LMS for Airtight System Security | null | https://www.ispringsolutions.com/ispring-learn/on-premise-lms | 2 | 1 | [
42014687
] | null | null | null | null | null | null | null | null | null | train |
42,014,691 | leizhan | 2024-11-01T07:14:22 | null | null | null | 1 | null | [
42014692
] | null | true | null | null | null | null | null | null | null | train |
42,014,701 | load1n9 | 2024-11-01T07:15:49 | null | null | null | 1 | null | null | null | true | null | null | null | null | null | null | null | train |
42,014,704 | chaosprint | 2024-11-01T07:16:10 | Norway's Wealth Tax Is Backfiring. Are Americans Paying Attention? | null | https://thedailyeconomy.org/article/norways-wealth-tax-is-backfiring-are-americans-paying-attention/ | 22 | 68 | [
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42,014,719 | eashish93 | 2024-11-01T07:19:55 | Show HN: Free web blocks and website design inspiration | null | https://webaggr.com/blocks | 1 | 0 | [
42014959
] | null | null | null | null | null | null | null | null | null | train |
42,014,727 | iowadev | 2024-11-01T07:22:15 | Show HN: Makr.io – 15 Open-Source Utility Apps Built with AI in 30 Days | Hi HN,<p>I recently completed Makr.io – 15 simple web apps, all open source on GitHub, created in just 30 days using Next.js, Vercel for hosting, and plenty of help from Claude and ChatGPT. Each app took me around 2-3 hours to build, with more time spent brainstorming ideas and framing problem statements.<p>Here’s my approach: I’d start with an idea and problem statement, then ask Claude for a detailed Python script to set up the project. Using the generated code as a foundation, I focused on refining essentials like mobile optimization and core functionality. This project was mostly built during early mornings and late nights as a personal challenge.<p>Here’s a sample of the apps:<p>SVG to PNG – Convert SVG files to PNG
Email Preview – Preview HTML emails
RSS Feed Reader – Read top RSS feeds
DMARC Checker – Check DMARC records
Event Countdown - Create countdowns for special days
Email header analyzer
HN client with dynamic sitemap<p>You can find the full collection on GitHub all open sourced. | https://github.com/renedeanda/makr.io | 3 | 3 | [
42041149
] | null | null | null | null | null | null | null | null | null | train |
42,014,734 | benreesman | 2024-11-01T07:23:18 | null | null | null | 1 | null | null | null | true | null | null | null | null | null | null | null | train |
42,014,735 | jamesmurdza | 2024-11-01T07:23:47 | null | null | null | 1 | null | null | null | true | null | null | null | null | null | null | null | train |
42,014,737 | tosh | 2024-11-01T07:24:36 | M4 Pro Geekbench | null | https://browser.geekbench.com/search?utf8=%E2%9C%93&q=mac16 | 3 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,014,753 | waveywaves | 2024-11-01T07:28:14 | Mirrord Mirrord on the wall, who's most processed of them all | null | https://vibhavstechdiary.substack.com/p/mirrord-mirrord-on-the-wall-whos | 12 | 4 | [
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42,014,775 | Leerick | 2024-11-01T07:33:52 | null | null | null | 1 | null | null | null | true | null | null | null | null | null | null | null | train |
42,014,787 | olayhabercomtr | 2024-11-01T07:36:56 | null | null | null | 1 | null | null | null | true | null | null | null | null | null | null | null | train |
42,014,791 | bontoJR | 2024-11-01T07:38:06 | Mac Mini with M4 Pro is the fastest Mac ever benchmarked | null | https://www.macrumors.com/2024/10/31/m4-pro-chip-benchmark-results/ | 98 | 53 | [
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42,014,795 | nomilk | 2024-11-01T07:38:24 | Is "adaptive" Wi-Fi speed a thing on modern machines? | null | https://superuser.com/questions/1860375/is-adaptive-wi-fi-speed-a-thing-on-modern-machines | 4 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,014,817 | thebeardisred | 2024-11-01T07:44:20 | It's been 30 years since Intel's infamous Pentium FDIV bug reared its ugly head | null | https://www.tomshardware.com/pc-components/cpus/its-been-30-years-since-intels-infamous-pentium-fdiv-bug-reared-its-ugly-head-a-math-bug-caused-intels-first-cpu-recall | 42 | 22 | [
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42,014,833 | pruthvikumar | 2024-11-01T07:49:18 | null | null | null | 1 | null | null | null | true | null | null | null | null | null | null | null | train |
42,014,852 | chistev | 2024-11-01T07:53:44 | null | null | null | 1 | null | null | null | true | null | null | null | null | null | null | null | train |
42,014,863 | skruger | 2024-11-01T07:56:18 | US Army should ditch tanks for AI drones | null | https://www.theregister.com/2024/10/30/google_ceo_tank_ai_drones/ | 1 | 2 | [
42015088,
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] | null | null | no_error | US Army should ditch tanks for AI drones, says Eric Schmidt | 2024-10-30T23:30:07Z | Dan Robinson |
Former Google chief Eric Schmidt thinks the US Army should expunge "useless" tanks and replace them with AI-powered drones instead.
Speaking at the Future Investment Initiative in Saudi Arabia this week, he said: "I read somewhere that the US had thousands and thousands of tanks stored somewhere," adding, "Give them away. Buy a drone instead."
Did we mention that Schmidt is the founder of a startup called White Stork that aims to develop AI-driven attack drones?
The former Google supremo's argument is that recent conflicts, such as the war in Ukraine, have demonstrated how "a $5,000 drone can destroy a $5 million tank."
In fact, even cheaper drones, similar to those commercially available for consumers, have been shown in footage on social media dropping grenades through the open turret hatch of tanks.
Schmidt, who was CEO of Google from 2001 to 2011, then executive chairman to 2015, and executive chairman of Alphabet to 2018, founded White Stork with the aim of supporting Ukraine's war effort. It hopes to achieve this by developing a low-cost drone that can use AI to acquire its target rather than being guided by an operator and can function in environments where GPS jamming is in operation.
Notably, Schmidt also served as chair of the US government's National Security Commission on Artificial Intelligence (NSCAI), which advised the President and Congress about national security and defense issues with regard to AI.
"The cost of autonomy is falling so quickly that the drone war, which is the future of conflict, will get rid of eventually tanks, artillery, mortars," Schmidt predicted.
Questions were being raised about the viability of modern battle tanks before the Ukraine war started, and with the Russian armed forces losing an estimated 3,000 in the first 24 months of the fighting, some military experts think the era of tank warfare is over.
However, both Russia and Ukraine have yet to ditch their tanks, with the latter country continuing to ask for Western hardware to help in its fight. The UK is also spending £800 million (about $1 billion) to upgrade its aging Challenger 2 battle tanks to Challenger 3, which comes with a completely new turret and gun, among other enhancements.
DARPA's latest toy is a 20-foot, 12-ton tank that drives itself
Drone maker DJI sues Pentagon over 'Chinese military company' label
South Korea orders 'Star Wars' lasers to blast Northern drones out of the sky
US Air Force secretary so confident in AI-controlled F-16s, he'll fly in one
Many modern tanks also sport active protection systems (APS) such as Trophy, which can detect incoming threats and typically launch explosive projectiles to intercept them before they reach the tank. Various other potential countermeasures are said to be in development, including laser dazzlers to blind the drone's sensors.
One military expert who spoke on the condition of anonymity told The Register: "Rumours about the main battle tank, heavy artillery, and infantry combat vehicles no longer having roles on the modern battlefield have been greatly exaggerated.
"Warfare, armies, change over time, new technologies arrive, others leave - horses no longer are frontline war-fighting assets. 'Drones' have shown greater utility than many had given them credit for before the Ukraine War, but they haven't shown themselves to be the absolute game changer that some had forecast. Sensible armies will opt for balance, and that balance will have both drones and tanks."
Despite what Schmidt suggests, the US Army isn't expected to mothball its thousands of M1 Abrams in favor of drones just yet. Instead - as our expert points out - it is likely to consider deploying both when called upon at some point. ®
| 2024-11-07T22:52:13 | en | train |
42,014,869 | frizlab | 2024-11-01T07:57:58 | Anthropic's Claude AI Chatbot Now Has a Mac App, but It's an Electron Turd | null | https://daringfireball.net/linked/2024/10/31/anthropic-mac-app-electron-turd | 9 | 2 | [
42016708,
42016435
] | null | null | no_error | Anthropic’s Claude AI Chatbot Now Has a Mac App, But It’s an Electron Turd | null | null |
Anthropic’s Claude AI Chatbot Now Has a Mac App, But It’s an Electron Turd
Emma Roth, reporting for The Verge:
Claude, the AI chatbot made by Anthropic, now has a desktop app.
You can download the Mac and Windows versions of the app from
Anthropic’s website for free.
Sebastiaan de With, on X:
Big miss from Anthropic releasing a super clunky macOS electron
app that feels like a bad wrapper of their website. Very weird
non-standard UI all over, choppy and sloppy animations.
OpenAI is really leagues ahead in making good apps (+ has ChatGPT
Search rolling out today).
There’s much talk that Anthropic’s Claude 3.5 has pulled ahead of OpenAI’s ChatGPT 4o in terms of chatbot “intelligence”, but as an overall experience ChatGPT wins hands-down. For one thing ChatGPT has been able to search the web for answers for a while now, and it works great. For another, just today OpenAI launched ChatGPT’s dedicated “search” mode. Claude has nothing like it.
But even their respective Mac apps are a stark contrast. The Claude app is a lazy Electron port. Right off the bat, the email field on the login screen doesn’t support autofill. Once you’re logged in, you don’t get any standard MacOS features. And of course because it’s Electron it’s bloated architecturally and uses a lot of memory. If you really want to use Claude as an “app” on your Mac you’d be better off saving a web app with Safari (File → Add to Dock…) than using this.
ChatGPT’s native Mac app, on the other hand, is a truly native Mac app. It looks like a Mac app and feels like a Mac app because it really is a Mac app. I’ve liked it ever since it launched back in May, and it keeps getting better. And I keep using it more and more as my go-to resource for answering questions.
I asked Claude, “What is the best way to engineer a native Mac app? What frameworks and developer tools should one use if the goal is a great Mac experience?” Claude’s answer started by positing it as a decision between SwiftUI and AppKit. Perhaps Anthropic’s Mac engineers should have asked Claude this same question before they built this turd of an Electron app.
★ Thursday, 31 October 2024
| 2024-11-08T14:02:08 | en | train |
42,014,872 | zabzonk | 2024-11-01T07:58:37 | Monkeys Will Never Type Shakespeare | null | https://www.bbc.co.uk/news/articles/c748kmvwyv9o | 8 | 2 | [
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42,014,873 | somanchiu | 2024-11-01T07:58:52 | Show HN: ReSwapper – Reproduce the Implementation of Inswapper | null | https://github.com/somanchiu/ReSwapper | 1 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,014,896 | Oliver_ | 2024-11-01T08:03:41 | null | null | null | 1 | null | null | null | true | null | null | null | null | null | null | null | train |
42,014,906 | dajonker | 2024-11-01T08:05:25 | Rewrite it in Rails | null | https://dirkjonker.bearblog.dev/rewrite-it-in-rails/ | 174 | 101 | [
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42,014,913 | huzhenjie751038 | 2024-11-01T08:06:44 | null | null | null | 1 | null | [
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42,014,918 | phughes1980 | 2024-11-01T08:07:11 | null | null | null | 1 | null | null | null | true | null | null | null | null | null | null | null | train |
42,014,930 | tosh | 2024-11-01T08:11:24 | WhiteBox | null | https://whitebox.systems/ | 3 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,014,980 | BerislavLopac | 2024-11-01T08:21:04 | What layoffs teach us about technical leadership | null | https://chelseatroy.com/2024/10/31/what-layoffs-teach-us-about-technical-leadership/ | 5 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,014,992 | willemlaurentz | 2024-11-01T08:22:54 | See the Milky Way with your own eyes (2018) | null | https://willem.com/blog/2018-08-21_space-travelling-from-el-teide/ | 1 | 0 | null | null | null | no_error | Space travelling from El Teide - Visiting Tenerife's volcano at the Canary Islands | null | Willem L. Middelkoop |
Visiting Tenerife's volcano at the Canary Islands
This summer holiday brought me to Tenerife, part of the Canary Islands, Spain. It's a popular destination because of the climate, but it's also the home of Teide, the world's second highest volcano (7500 m, measured from the sea floor). The incredible views reach way beyond earth, into outer space, the Milky Way and other distant galaxies.
TenerifeThe Canary Island of Tenerife is known internationally as the "Island of Eternal Spring" (Isla de la Eterna Primavera). At about the same latitude as the Sahara Desert, it enjoys a warm tropical climate with an average around 24 °C. The climate is controlled by tradewinds on the Atlantic Ocean, whose humidity condenses over the northern part of the island, creating clouds. The weather on the island is different on the North and South because of the enormous volcano in the centre.Radar image from NASA showing the Teide volcano on the island of Tenerife, Canary Islands, Spain (Wikimedia Commons)The radar image shows the Teide volcano on the island of Tenerife in the Canary islands. The image was created using the Spaceborn Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR) onboard the space shuttle Endavour in 1994. The colour indicates differences in terrain, where shades of green and brown indicate lava flows of various ages and roughnesses. El TeideThe volcano on the island of Tenerife, El Teide, is the highest in the world next to the Hawaiian Islands. Measured from the ocean floor, its top (Pico del Teide) reaches 7500 meters. Its summit is 3718 meters above sea level. Because of its history of destructive eruptions, the volcano is closely monitored by the United Nations Committee for Disaster Mitigation. Its last eruption was in 1909. This detailed astronaut photograph features two stratovolcanoes, Pico de Teide and Pico Viejo (taken from the International Space Station, public domain)The Las Cañadas caldera on top of Teide, as seen from the International Space Station (public domain)If you look closely, you'll notice that it appears as if the volcano has collapsed: its summit seems to be pushed downwards, surrounded by a "ring" of mountain tops. This is called a "caldera", a large collapse depression usually formed when a large eruption completely empties the magma chambers inside the volcano. Teide seen from my iPhoneThe volcano and its surroundings comprise the Teide National Park, a World Heritage Site by UNESCO. Its environmental conditions and geological formations are similar to those on Mars. My space mobile, a Citroën C1 rental car... The Teide national park is open for public and accessible by road. Along the way you're in for some fantastic views.Twisty roads to the top of Teide, with remarkable geological formations visibleLandscape inside the Las Cañadas caldera is similar to MarsThe landscape is unlike anything I have seen before. Because of the similarities with Mars, Las Cañadas is used by space agencies to test planetary rovers. ESA's fast-moving Heavy Duty Planetary Rover (HDPR) at Teide (credit: ESA)Shadow of a Martian... of sorts :-)Some rocks are unbelievable in size and shapePico del Teide as seen from the south of the calderaThe El Chinyero vent on the Santiago Ridge, still black from the 1909 eruptionPanorama in the Las Cañadas calderaThere are four main roads to and from the caldera, each one offering different scenes. From dense woods to bare rocks, from heavy fog to clear skies. You should explore them all!On top of the Los Gigantes massive, with the La Gomera island on the horizonThe Los Gigantes are really unbelievable in their dimensions, you'll feel like a tiny human on the twisty roads.Driving above the cloudsIf the extra terrestial landscape isn't enough, driving above the clouds really makes you realise this ain't your typical commute. The contrast with the top of Teide is amazing when you go under the clouds. There the landscape is green, full of life and vegetation. Under the clouds the landscape is green, full of lifeEl Palmar in the valley between Monte del Agua and the Teno mountainsIf you follow the Masca hiking trail - which is a dangerous walk - you'll be able to go down, all the way to sea level. If you don't feel like mountaineering, you can reach this phenomenal swimming place by boat, spotting whales along the way. Swimming in the Atlantic Ocean, down at Los GigantesBut, be careful where you decide to take a plunge into the ocean! At the north side of the island there are strong currents, highly dangerous for swimmers.Rescue helicopter lifting persons out of the ocean, notice the volcanic black beachSpaceBecause of the good astronomical seeing conditions on the top of the volcano, Teide is used to look at the stars. Ever since 1964, it's home to the Teide Observatory (Observatorio del Teide). It became one of the first major international observatories with telescopes from different countries. Its quite the view, this "telescope street". Teide Observatory with telescopes from different countries at 2390 meters altitudeAstronomical observatories are generally situated on top of mountains, as the ground air is usually more convective (turbulent because of the temperature). Stable air from high above the clouds and oceans generally provides the best seeing conditions. It's also very dark during the nights at Teide, because it's located in the middle of the ocean (limiting light pollution from big cities). You should really try it yourself, visit Teide at night. The sky is really unbelievable: I have never seen so many stars! You don't need a telescope or special glasses, just look with your bare eyes. Most stars and galaxies can be seen before the moon rises. Take your time and you'll notice even more detail. Me and the Milky Way (photo by Ellen Middelkoop)This stunning photo was taken by wife Ellen using a professional camera. It very much reflects how the sky actually is during the night at Teide - this is no Photoshop! See the Milky Way light up the sky like a subtle cloud. Billions of stars in one view. Never ever have I seen something like this before, it is truly magnificent. ConclusionYou don't have to wait for Elon Musk to complete his Mars rocket to see outer space. Visit Tenerife and be amazed by the extra terrestrial views.Relaxing in the poolAfter all your stargazing and space travelling, you should relax and enjoy the sunny weather from the swimming pool. Happy holidays, everybody!
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| 2024-11-08T09:56:42 | en | train |
42,015,005 | hackernj | 2024-11-01T08:25:42 | Universe would die before monkey with keyboard writes Shakespeare, study finds | null | https://www.theguardian.com/science/2024/nov/01/infinite-monkey-theorem-keyboard-tyepwriter-shakespeare-study | 83 | 158 | [
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] | null | null | no_error | Universe would die before monkey with keyboard writes Shakespeare, study finds | 2024-11-01T05:32:03.000Z | null | Mathematicians have called into question the old adage that a monkey typing randomly at a keyboard for long enough would eventually produce the complete works of Shakespeare.Two Australian mathematicians have deemed it misleading, working out that even if all the chimpanzees in the world were given the entire lifespan of the universe, they would “almost certainly” never pen the works of the bard.The paper tested the “infinite monkey theorem”, a thought experiment demonstrating that an infinite amount of time can make something that is incredibly unlikely become probable, by asking what would happen if generous yet finite limits were placed on the monkey typists.Their calculations were based on a monkey spending about 30 years typing one key a second at a keyboard with 30 keys – the letters of the English language plus some common punctuation. It found that the time it would take for a typing monkey to replicate Shakespeare’s works would be longer than the lifespan of our universe.The “heat death” of the universe was assumed to take place in around a googol of years – that is a one followed by 100 zeroes. Other more practical considerations – such as what the monkeys would eat, or how they would survive the Sun engulfing Earth in a few billion years – were set aside.There was only around a 5% chance that a single monkey would randomly write the word “bananas” in their lifetime, according to the study in the journal Franklin Open.Shakespeare’s canon includes 884,647 words – none of them banana.To broaden out the experiment, the Australian mathematicians turned to chimpanzees, the closest relative of humans. There are currently about 200,000 chimps on Earth, and the study presumed this population would remain stable until the end of time.Even this massive monkey workforce fell very, very short.“It’s not even like one in a million,” study co-author Stephen Woodcock of the University of Technology Sydney told New Scientist. “If every atom in the universe was a universe in itself, it still wouldn’t happen.”And even if many more chimps who typed much quicker were added to the equation, it was still not plausible “that monkey labour will ever be a viable tool for developing written works of anything beyond the trivial”, the authors wrote in the study.A previous trial into the thought experiment, which gave six Sulawesi crested macaque monkeys four weeks with a computer, produced just five pages of text, primarily filled with the letter S. | 2024-11-08T06:44:55 | en | train |
42,015,006 | doener | 2024-11-01T08:25:45 | null | null | null | 1 | null | null | null | true | null | null | null | null | null | null | null | train |
42,015,047 | matthiasstiller | 2024-11-01T08:36:27 | Addressing Innovation in Textual Communication | null | https://github.com/fvonx/nela | 2 | 3 | [
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42,015,077 | alixwang | 2024-11-01T08:43:13 | Show HN: Cronbuilder.online cron expression builder with AI | null | https://cronbuilder.online | 2 | 0 | null | null | null | missing_parsing | cron ai builder online | null | null | ContactBuild Your PerfectCron Expression Effortlesslydayevery hourevery minuteHow it worksDescribe your cron expression in natural languageOur AI will generate the cron expression for youCopy the cron expression and use it in your cron job | 2024-11-07T22:55:28 | null | train |
42,015,080 | hunglee2 | 2024-11-01T08:44:05 | Prejudice and China | null | https://research.gavekal.com/article/prejudice-and-china/ | 5 | 1 | [
42015169
] | null | null | no_error | Prejudice And China - Gavekal Research | null | null |
Prejudice And China
Gavekal Research
| 22 Oct 2024
At an investment conference in Kuala Lumpur recently, I caught up with an old friend and Gavekal client. Over coffee between sessions, we talked about one of the most visible changes of the last few years in Asia: the Chinese cars that have so quickly appeared on roads across the continent. This led us to the comments made in September by Ford chief executive officer Jim Farley. Freshly returned from a visit to China, Farley told The Wall Street Journal that the growth of the Chinese auto sector poses an existential threat to his company, and that “executing to a Chinese standard is now going to be the most important priority.”
By any measure, this is an earth-shattering statement.
Making cars is complicated. Not as complicated as making airliners or nuclear power plants. But making cars is still the hallmark of an advanced industrial economy. So, the idea that China is suddenly setting the standards that others must now strive to meet is a sea-change compared with the world we lived in just five years ago.
This led my friend to question how Farley and other auto industry CEOs could have fallen quite so deeply asleep at the wheel. How could China so rapidly leapfrog established industries around the world without all those very well paid Western CEOs realizing what was happening until two minutes ago?
There are many possible answers to this question. They range from the obvious through the historical and cultural to the tin-foil hat variety. And they are well worth reviewing in an attempt both to understand where China is today, and to highlight the blind spots some investors still suffer from when looking at the world’s second largest economy and their implications for markets.
The obvious explanation: Covid, Ukraine, DEI and ESG
Gavekal’s head office is in Hong Kong. But we also have an office in Beijing, with a great team of analysts who publish excellent work (at least, I like to think so). I do not want to sound as if I am bragging (even though I am), but for years our Beijing office would host at least one visitor from abroad every day. I wouldn’t claim that Gavekal was a mandatory stop for every portfolio manager and CEO visiting Beijing. That would make me sound like a conceited jerk. But for many of Gavekal’s clients and their friends, it really was true (that we were a mandatory stop, not that I am a conceited jerk).
Then Covid hit. For three years, no visitor crossed our threshold. By the time the Chinese government finally lifted its Covid restrictions, Russia had launched its “special military operation” in Ukraine. This meant that for most Westerners, China had become uninvestible. The visitors stayed away. The end of Covid restrictions barely made a mark on our Beijing conference room’s planning schedule.
This brings me to the simplest, most obvious, and likeliest explanation why most CEOs and investors missed how China leapfrogged the West in industry after industry over the last five years: during that time, no one from the West bothered to visit China. Consequently—and perhaps more by accident than design—China followed Deng Xiaoping’s advice to “secure our position; cope with affairs calmly; hide our capacities and bide our time; keep a low profile and never claim leadership.”
To be fair, it wasn’t just that visiting China was difficult—even impossible—for much of the last five years; foreign CEOs had a lot on their plates. Covid restrictions forced company managements to come up with new ways to work on the fly. There were also massive supply chain disruptions to contend with. And some of these were greatly compounded by the Russia-Ukraine conflict.
Consider a car company CEO: after spending a few quarters figuring out how to rearrange factory work to comply with social distancing, he or she suddenly had to worry about the supply of platinum coming out of Russia, or of neon coming out of Ukraine. This might help to explain how car company CEOs missed how rapidly Chinese autos were gaining in their rearview mirrors.
And of course at the same time, many CEOs were trying to keep up with ever-multiplying diversity, equity and inclusion standards and environmental, social and governance requirements.
Diversity is a strength. But unfortunately, it could be that all the focus on diversity has not strengthened Western industries quite enough to cope with the oncoming Chinese onslaught. Hence Western policymakers’ enthusiasm for executing a 180˚ U-turn, and instead of promoting free trade and the beauty of Western liberalism, suddenly imposing tariffs and building walls.
Or to put it less kindly, while Western CEOs focused on virtue-signaling, Chinese companies forged ahead, producing better products for less money— which is what capitalism should be about. Today, we are seeing the results.
The cultural and political prejudice explanation
A second possible reason the West failed to spot how it was being leapfrogged by Chinese industry could simply be good old-fashioned ingrained cultural prejudice. It may be unkind to highlight it, but history has shown that Western leaders repeatedly underestimate their Asian competitors.
Russian Tsar Nicholas II infamously thought his army and navy would quickly defeat the Japanese, only for his army to suffer successive defeats and for his navy to be destroyed at Tsushima in 1905.
Winston Churchill and the British military’s chiefs of staff never thought the Japanese army capable of advancing so swiftly down the Malay peninsula and positioned Singapore’s big guns facing the wrong way.
Douglas MacArthur and the US general staff underestimated their opponents’ resolve in the Korean war.
The French establishment did the same in Indochina.
Lyndon Baines Johnson and Robert McNamara did the same in Vietnam.
US automakers initially laughed off Japanese competitors.
The “West” underestimating the “East” is a fairly strong constant of history (for more on this, I cannot recommend highly enough the 1963 book East And West by Cyril Northcote Parkinson). This time around, the underestimation may have been compounded by China’s official name—the People’s Republic of China—and the country’s political structure as a communist one-party state. To any self-respecting Western capitalist, the word “communist” implies inefficiencies, poor products, and technological backwardness.
This belief was amply demonstrated by the fall of the Berlin Wall and the collapse of the Soviet Union. By now, the PRC has survived longer than the USSR’s 74 years. Nevertheless, most Westerners still believe that at some point in the not-so-distant future, the Chinese Communist Party will lose its grip on power, just like the Communist Party of the Soviet Union. How could it be otherwise? It’s all in the name. Communism is bound to fail.
This assumes, of course, that China really is communist; a notion that could be debated. It also ignores the old adage that “the tragedy of Asia is that Japan is a profoundly socialist country on which capitalism was imposed, while China is a profoundly capitalist country on which socialism was imposed. But each will naturally drift back to its natural state.”
Recent anchoring and the Japan explanation
Another explanation for the Western blind spot on China’s industrial progress might well be the last three “lost decades” of Japanese growth. This shows up in investor’s responses to the China’s stimulus. Conversations about China’s growth predicament typically start with the assumption that without massive fiscal stimulus, China will be unable to get out of its current economic rut. This is because China resembles Japan 20 or 30 years ago, with (i) terrible demographics and (ii) widespread large losses across the real estate sector.
However, this is probably where the similarities end. Unlike Japan in the 1990s, China has not seen its banking system go bust and lose its ability to fund new projects. On the contrary, the surge in loans to industry over the past few years lies at the heart of China’s booming industrial productivity.
Interactive chart
This is another key difference between China today and Japan in the 1990s. China today is not only more efficient and more productive than a decade ago, it is probably more efficient and more productive than most other major industrial economies. And it boasts a very attractive cost structure. Until a few years ago, you would need to check your bank balance before going out for dinner in Tokyo. Today, you can stay in the Four Seasons in Beijing or Shanghai for less than US$250 a night. Perhaps the best illustration of how Japan’s past is a very poor guide to China’s present is the difference in their trade balances; a reflection of how different their competitiveness has been.
Interactive chart
This is not to understate the magnitude of the Chinese property bust. The rollover in real estate has been a massive drag on growth and on animal spirits over the past five years. But on this front, there is another key difference between China and Japan: in China, the contraction of real estate was the policy. It was not the unfortunate consequence of policies gone-wrong. Reallocating capital away from real estate and towards industry was a stated goal of the government. This is clear from the chart on bank lending.
The pain of the property bust is also clear in the consumer confidence data. As discussed in past reports, the rollover in real estate has hit millennials living in first and second-tier cities disproportionately hard (see Stimulus And Confidence In China or Chinese Stocks Are For Living In). This hit to confidence might help partially explain the Western blind spot on China’s recent industrial progress.
The ‘it depends who you talk to’ explanation
The table below illustrates how two groups in China feel particularly unhappy.
Older folks living in the countryside—the “left behind” in China’s mad rush towards modernity.
Millennials living in first and second tier cities—the “bag-holders” in China’s real estate consolidation.
Importantly, millennials in first tier cities also happen to be the group that most Westerners who have contacts in China typically talk to. This is the group that speaks English (older folks were seldom taught English at school) and that grew up using social media. It is the group that was spared the hardships of the cultural revolution, and did not experience the trauma of 1989, and which therefore tends to be more vocal.
This group has had little positive to report over the past five years. Their time has been tough. First, their balance sheets were hammered by falling real estate prices. Second their income prospects have been capped by the rapidly rising numbers of Gen-Z graduates churned out by China’s universities. In short, being a millennial in a first tier city has not been a fun experience in recent years.
Meanwhile, people living in third and fourth tier cities talk about the better-paying jobs in the growing factories, the improved municipal and regional infrastructure and the high-speed trains that link their towns to China’s mega-cities. To put it more succinctly, there have been two main stories in China over the past five years. The first was a real estate bust, which was felt disproportionately in the rich cities of China’s coast. The second was an impressive industrial boom, which had a greater impact on the cities of the interior with cheaper labor which were suddenly linked to the coast by new highways, railways and airports.
Over the past five years, consumers of Western media have heard a lot about the first trend; very little about the second.
The ‘maybe the media covered the wrong trend’ explanation
Over the past few years, I have argued at length that the relentlessly negative coverage of China by the Western media was doing its readers a disservice. This is not to say that China does not have serious problems to confront and major challenges to overcome. But by disproportionately focusing on these, Western media helped their readers to develop a massive blind spot when it came to China’s global economic and geopolitical impact.
Instead of collapsing into economic irrelevance, currency devaluation and a “shadow banking” meltdown (remember that one?), China has continued to make progress along the path it set for itself over a decade ago: tying ever more emerging markets into China’s economic orbit, settling more of its trade in its own domestic currency, bypassing Swift, fostering energy independence, and moving up the export value chain.
All these trends were both predictable and predicted. So how did the Western media manage almost entirely to ignore them? Why were there so few stories about how China now installs almost twice the number of industrial robots as the rest of the world combined? Or on China’s new status as the global leader in the nuclear industry? Or on how China graduates more engineers each year than the entire OECD?
The simplest explanation is that the media is in the “bad news” game. The old adage “if it bleeds, it leads” still holds good in most editorial conferences. So, in a click-obsessed world, stories about ghost cities and impending economic doom are bound to get more traction than features about educational progress, revolutionary drones or factory automation.
A second possible explanation is linked to our own equity-market-obsessed culture. It is hard to go anywhere in the US—airport lounge, hotel lobby, sports bar—without a screen in the background playing either CNBC or Bloomberg TV with the day’s stock quotes filing by. In Europe, stock prices aren’t quite so “in your face,” although you can still feel their presence. And in an equity-market-obsessed culture, the performance of the stock market index is quickly equated with the performance of the economy at large.
Of course, in most emerging markets, the relationship between economic progress and equity prices is tenuous at best. China is a great example. China’s economic progress over the past five, 10 and 20 years is undeniable, with collapsing infant mortality, increasing life expectancy, soaring educational attainment, the build-out of new infrastructure and enormous productivity gains across a broad swath of industries. But broad equity market returns as measured by key indexes have been pedestrian at best.
Interactive chart
For an equity-obsessed culture, it is tempting to look at China’s disappointing stocks market performance and conclude that if stocks are not doing well, then something must be wrong with the underlying economy. But just because it’s tempting doesn’t mean it is right.
The tin-foil hat explanation: the user is the product
It is one of my deeply held beliefs that media organizations continue to charge viewers and readers for access, whether through streaming service subscription fees or just the few dollars needed to buy a newspaper or magazine, in order to give the impression to the end user that he or she is still the client. However, the true clients are the health care industry (one of the largest advertisers in the US), the luxury goods industry (another giant advertiser), the automobile industry (same again) and—perhaps most worrying—governments everywhere.
In some countries, such as France, governments have always doled out generous subsidies to the press. In other countries less so—at least in the past. But in many countries, Covid changed the relationship between governments and media. Governments took out full-page advertisements to remind people to wash their hands, keep their distance from each other, and to participate in an enormous health experiment. And, call it a miracle, but for their part the media almost entirely failed to question the unprecedented way governments trampled all over age-old civil rights and personal liberties.
Unfortunately, history shows that once latched on, it is difficult for anyone to ween themself off the government’s generous breast. This is where the happy—for the media—news of HR 1157 comes in. On September 9, the US House of Representatives approved a bill entitled “Countering the PRC Malign Influence Fund Authorization Act” by 351 votes to 36.
If passed by the Senate, this bill will authorize the US government to spend US$325mn a year every year for the next five years to “support... independent media to raise awareness of and increase transparency regarding the negative impact of activities related to the Belt and Road Initiative, associated initiatives, other economic initiatives with strategic or political purposes, and coercive economic practices.”
So yes, at a time of record debt and swelling budget deficits, the US government proposes to spend US$325mn a year paying “independent” media (the irony!) to push stories about the negative impact that China may be having around the world.
As Charlie Munger liked to say “show me the incentives, and I will tell you the outcome.”
If the US government is openly declaring that it will pay for negative stories on China in “independent” media, and allocating millions of US dollars to this purpose, should we be surprised if negative stories about China are precisely what the media delivers?
So, now more than ever before, when assessing stories in the media it is helpful to ask the question: just who here is the client, and who is the product?
Three Chinas
Putting all this together, there seem to be at least three separate visions of China.
The first is the China you read about in much of the Western media: a place of despond and despair. It is permanently on the cusp of social disorder and revolution, or it would be, were it not an Orwellian nightmare of state surveillance, supervision and repression that strangles creativity and stifles progress. This is the place that Westerners who have never visited China typically imagine, because it is the place portrayed by the media.
And not just by the media. This is also the China portrayed by large parts of the financial industry. Every 10 days or so, I get forwarded another report forecasting the imminent collapse of the Chinese economy. More often than not these are written by Western portfolio managers who typically don’t speak Chinese, know very few people who live in China, and in some cases have never even visited what is very clearly the most productive economy in the world today. This has happened so often, I have made a meme about it.
This is the vision of China that allowed CEOs of Western industrial companies to spend their time worrying about DEI initiatives while Chinese companies were racing ahead of them.
The second is the vision of China you get from talking to Chinese millennials in tier-one cities. This version of China recalls the “lost decades” of Japanese deflationary depression.
Clearly, for investors there are important differences between China today and Japan of the 1990s and 2000s. First, in 1990, Japan was 45% of the MSCI World index even though Japan accounted for only around 17% of global GDP. Today, Chinese equities make up less than 3% of the MSCI World, even as China is around 18% of world GDP. So, it seems unlikely that foreign investors will spend the coming years running down their exposure to China; few have much exposure to China in their portfolios to begin with.
Second, China’s dominance in a number of important industrial segments is growing by leaps and bounds. This is a reflection of the rapidly changing geopolitical landscape. In 2018, Donald Trump’s decision to ban the sale of high-end semiconductors to China acted as a galvanic shock on the Chinese leadership. If semiconductors could be banned today, tomorrow it might be chemical products or special steels. Protecting China’s supply chains from possible Western sanctions became a priority to which almost everything else (aside from the currency and the bond markets) was a distant second.
This brings me to the third vision of China: that it is only just beginning to leapfrog the West in a whole range of industries. This vision is starting to show up itself in the perception of Western brands in China, and their sales. For example, Apple’s iPhones no longer figure in the five best-selling smartphone models in China. And Audi’s new electric cars made and sold in China will no longer carry the company’s iconic four-circle logo; the branding is now perceived to be more of a hindrance than a benefit.
To put it another way, following years of investment in transport infrastructure, education, industrial robots, the electricity grid and other areas, the Chinese economy today is a coiled spring. So far, the productivity gains engendered by these investments have manifested themselves in record trade surpluses and capital flight—into Sydney and Vancouver real estate, and Singapore and Hong Kong private banking.
This has mostly been because money earners’ confidence in their government has been low. From bursting the real estate bubble, through cracking down on big tech and private education, to the long Covid lockdowns, in recent years the Chinese government has done little to foster trust among China’s wealthy. It’s small surprise, then, that many rich Chinese have lost faith in their government’s ability to deliver a stable and predictable business environment.
This brings me to the recent stimulus announcements and the all-important question whether the measures rolled out will prove sufficient to revitalize domestic confidence in a meaningful way. Will it even be possible to lift confidence as long as the Damocles’ sword of a wider trade conflict with the US and yet more sanctions looms over the head of Chinese businesses?
From this perspective, perhaps the most bullish development for China would be for the new US administration (regardless who sits behind the Resolute desk) to come in and look to repair the damage done to relations by the 2018 semiconductor sanctions and the 2021 Anchorage meeting (see Punitive Tariffs Or Towards A New Plaza Accord?). At the risk of mixing metaphors, this could be the match that lights the fuse that ignites a real fireworks show.
In the meantime, the dynamics in China can perhaps best be summarized by the following decision tree.
Investment conclusions
The narrative around China is shifting—regardless of the US$325mn that the US Congress is looking to spend each year to fund negative stories about China in the “independent” media.
Just a few weeks ago, China was still said to be uninvestible. This view had led many people, including prominent Western CEOs, to conclude that China no longer mattered. This was a logical leap encouraged by Western media organizations, whose coverage of China has been relentlessly negative. It was a leap that turned out to be a massive mistake.
When it comes to China’s relevance to investors, there are four ways of looking at things.
China can be uninvestible and unimportant. This is the pool that most investors have been swimming in for the last few years. But this is akin to saying that China is like Africa. It simply doesn’t pass the smell test. Instead of sliding into irrelevance, China’s impact on the global economy only continues to grow.
China can be uninvestible but important. This is essentially what Jim Farley, fresh back from his China trip, told The Wall Street Journal.
China can be investible but unimportant. This is the space Japan inhabited for a couple of decades, and into which Europe seems to be gently sliding. However, the idea that China today is where Japan has been for the last three decades is grossly misplaced on many fronts, including the competitiveness of its economy, its overall cost structure, and its weight in global indexes.
China can be investible and important. This is what David Tepper of Appaloosa Management argued on CNBC following the announcement of China’s stimulus (see Changing Narratives Around The World). For now, this is still a minority view, at least among Western investors. Not that Western investors matter all that much. What truly matters is whether Chinese investors themselves start rallying to this view. If they do, the unfolding bull markets in Chinese equities and the renminbi could really have legs.
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| 2024-11-08T08:48:44 | en | train |
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42,015,127 | timbilt | 2024-11-01T08:52:54 | Enabling Infinite Retention for Upsert Tables in Apache Pinot | null | https://www.uber.com/en-PL/blog/enabling-infinite-retention-for-upsert-tables/ | 1 | 0 | null | null | null | null | null | null | null | null | null | null | train |
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42,015,138 | jonbaer | 2024-11-01T08:54:13 | North Korea Missile Test Visualization | null | https://nagix.github.io/nk-missile-tests/ | 2 | 0 | null | null | null | no_error | North Korea Missile Test Visualization | null | null |
This data visualization was produced by Akihiko Kusanagi. The data for this visualization are sourced from the CNS North Korea Missile Test Database, which is the first database to record flight tests of all missiles launched by North Korea capable of delivering a payload of at least 500 kilograms (1102.31 pounds) a distance of at least 300 kilometers (186.4 miles). The database captures advancements in North Korea's missile program by documenting all such tests since the first one occurred in April 1984, and will be routinely updated as events warrant.For more info and source code, please see the GitHub repository.
| 2024-11-08T02:56:55 | en | train |
42,015,154 | willemlaurentz | 2024-11-01T08:57:39 | Replace your phone with a smartwatch (2023) | null | https://willem.com/blog/2023-11-10_apple-watch-as-phone/ | 1 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,015,165 | cranberryturkey | 2024-11-01T09:00:07 | Release v2.0.4 · Denoland/Deno | null | https://github.com/denoland/deno/releases/tag/v2.0.4 | 1 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,015,170 | austinallegro | 2024-11-01T09:00:46 | Exploring the Cefucom-21 Japanese English Language Teaching Computer | null | https://ctrl-alt-rees.com/2024-10-18-exploring-the-cefucom-21-japanese-english-language-teaching-computer.html | 1 | 1 | [
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] | null | null | Failed after 3 attempts. Last error: Quota exceeded for quota metric 'Generate Content API requests per minute' and limit 'GenerateContent request limit per minute for a region' of service 'generativelanguage.googleapis.com' for consumer 'project_number:854396441450'. | Exploring The Cefucom-21 Japanese English Language Teaching Computer | 2024-10-18T06:00:00+00:00 | Rees |
Watch on YouTube: https://www.youtube.com/watch?v=11sqTJXXSQo
Introduction
Script
Original Video Links
Introduction
This is the Cefucom-21, an English language teaching computer from Japan - and it’s very weird. So let’s see what it’s all about!
Script
I think it’s fair to say that I have something quite interesting to show to you today. You see, I won this machine on one of the Japanese auction sites a couple of weeks ago, and in the few weeks that I’ve owned it, it’s only raised a lot more questions than answers. For example, this thing on the side, which I assumed from the photos was some kind of screen - like a CRT or an LCD - turns out it actually isn’t, although it is a display of sorts, and of course a bit later on in the video we will have a look at how that works.
But for now I wanted to put this out there not only as an initial exploration of the hardware and sharing the few bits of information that I have managed to track down, but also as a call for help really, for anyone else out there who might know a bit more about these machines than I do. I have tracked down one person in the UK who owns one of these - in fact he actually owns two of them - and he has been incredibly helpful in sharing photos of some of the extra bits and bobs and the information that he’s managed to put together, but it turns out that neither of his machines actually work either, so he was very keen to see whether this one worked - and we’ll also talk about that a bit later on in this video.
But for now, Let’s have a look around it and try and work out what it’s all about.
So, what exactly is this thing? Well, it’s a Cefucom or Cefucom-21, and on the front it says “CCI Multipurpose SLAP Computer”. Now, I’m not quite sure what SLAP actually stands for, but I do know that it is something to do with language learning, because this was actually sold in Japan as an English language learning solution.
Hardware-wise, it’s based on and reportedly 100 percent compatible with the Sanyo PHC-25, and if you’re familiar with that machine you will no doubt have noticed that the keyboard looks very, very similar indeed.
According to 1000bit.it, which I will link down in the description along with any other information I have, the specs of this thing are quite, quite interesting - so it was released in 1983 and supposedly has 52 kilobytes of RAM, with 6 kilobytes of VRAM. It has a Z80 CPU running at 2 megahertz - or is that two Z80 CPUs running at an unknown number of megahertz, I’m not quite sure, hopefully we can find out in this video - and it runs Cefucom Basic 1.0 and reportedly has one graphics mode, which is 256x192 at nine colors.
Finally, it has a built in tape deck that supposedly supports a data rate of 1200bps, it has a Centronics parallel port - and mine actually has a few extra ports on the back as well, including joysticks, which is quite interesting - and it supports RF and composite video output.
You’ve no doubt also noticed the port labelled “Coupler” in this area, and there’s actually a power pass through for this, where the main power cable comes into the unit, along with pass throughs for a printer and a TV. Now this actually came with its original manual - in fact, believe it or not, it was in the original box, with the original polystyrene and the original plastic bag and all the accessories as well - so I don’t even know if this has ever actually been used. But on the cover of said manual, there is a picture of this machine next to a matching acoustic coupler and printer - so it would make sense that they were available as accessories. The coupler, of course, being a type of modem where you would place a phone on the top and it would allow it to connect to other machines or perhaps some kind of network service.
On the side is a port labelled “ROM Cartridge”, which I can only assume allows it to load those Sanyo compatible cartridges, but it seems that the software for this was mainly distributed on cassette tape.
So, I mentioned earlier that I’d been in touch with another UK-based Cefucom-21 owner, and he goes by the name of “HereBeDragons” on social media - he’s one that you may have come across, he’s also heavily involved with the Centre for Computing History in Cambridge, where he organises some quite cool events and things. Also, a massive fan of the Dragon32, that amazing computer from Wales, which I have covered previously on this channel - and he has provided some very useful information and some photos that go along with this - so he managed to track down the original software packs for this which were shipped on cassette - and there are some interesting looking photos here - so they came in these blue binders and they had titles like “Using Personal Computers” and “Daily Life” and that kind of thing.
But I think the really interesting and unique thing about this machine, which we’ve managed to piece together, is to do with this rather chunky looking cartridge - and indeed, there’s also a picture of this in the manual that kind of shows how it works - so as you can see, it has like a cartoon image on the front, and a couple of what I would describe as gears or sprockets on the end - and the internal mechanism built into the Cefucom-21 looks like something out of a printer or something and evidently kind of drives this thing directly, so what we think happens is that as you progress through the English language lessons, there’s an accompanying picture that comes up on the screen and it’s basically an endless loop of paper that has all of these images printed on it and the computer just winds its way through them as the lessons progress - and this is in addition to the image that would be displayed on the TV screen as well, of course - so, a really, really interesting and unique setup.
Again, big thanks to Tony for those additional images and also the additional information. It’s been absolutely invaluable in trying to work out just what the Cephucom 21 was all about.
Unfortunately, along with those images, he also sent a warning - and as you can see, this is to do with the internal battery. Now, sadly, this is all too often the case with these older machines - the battery leaks over time and can do quite a lot of damage to the PCB, and that has indeed happened to his machine so I absolutely, as a top priority, need to get the battery out of this machine. But to be honest, that gives us an opportunity to have a poke around inside and see how all of that works as well - so let’s do that now!
And this thing is quite large, as you can probably see, and it has a lot of screws in the bottom - in fact, the only screws I can see on it are on the bottom here - so I’m guessing this whole bottom part comes off.
I’ll just remove the cartridge slot. So we have the audio connectors here, and of course there are wires that go to those, but that looks like that board should just unclip, I think.
Yep, that’s easy enough - and there is a ribbon cable going to the cartridge port.
How does that work?
Ah! Easy peasy, so that just unclips as well. Very nice design, actually - quite serviceable.
Ah, okay, so as I was unscrewing the screws, I did hear something drop down inside on a few of them, and it would have been these plastic blocks. It looks like that these were once glued onto the sides there, and evidently the glue’s given up over time so they should be easy enough to glue back on - and it looks like we have a few of those.
The front screws went straight into there and thankfully they are all the same length, apart from the screws that went into the motherboard here - and there was one kind of here- it must have been this one- kind of here-ish as well. Maybe as we take these boards out we can rescue those plastic blocks. So what else do we have here? We have a big transformer, which of course is connected to the mains input - so that all makes sense. We have a fuse here, that’s the cassette mechanism that we saw on top, and this is that screen mechanism - and it looks like, ah, it looks like there was something belt driven here, and that looks like the remains of a belt, which is not good!
But that’s, to be fair, that’s probably come from the the tape mechanism.
Interesting - and there’s like an elastic band here - this is for driving this screen thing, this paper cartridge mechanism here - and that looks okay. That’s actually intact so I won’t touch that.
I would like to know where that belt came from…
But of course, our job for today is to track down that battery - I’m not quite sure which of these boards it’s on - you probably can’t see from this camera angle, but there are actually one, two, three, four stacked boards here with risers between them - so I’ll get all of those out and we’ll see where we are.
Ah, this is very good news indeed - so this is the battery and we have some leakage just starting on one end, but it hasn’t actually spread as far as the rest of the board - and the other end is perfectly fine so that is looking great - so I’ll get that out in a second, I’ll get the soldering iron out and desolder that, but first let’s just have a look at the rest of this board and see what we can see.
Okay, so I’m back, I’ve been Googling datasheets frantically, and I think I’ve worked out what’s going on with this board here - so, this is indeed the brains behind the actual operation, this is our Sanyo PHC-25 clone, and the board is completely different to the board in the Sanyo, so it’s not like they bought a job lot of Sanyo PHC-25 motherboards and just stuffed them into these machines, this is very much a custom designed thing. We have that Z80 CPU just here, and from what I can see, there’s only one of those, at least on this board - there might be additional CPUs on these other boards for controlling other things, I’m not quite sure - and we have this chip here as well, which is the PIO chip for the Z80, so that interfaces it with various other things on here.
Supporting those we have these LH0082As, and these are Z80 CTCs - so these are timing chips, there are two of these, and they just handle the timing of everything on this board to keep everything in sync.
Over here we have two SRAM chips - so these are asynchronous static RAM chips and they are 16K each - so of course 32K in total. We have three of these programmable peripheral interface chips here, which I’m guessing wouldn’t have been present in the Sanyo - and of course, these are to interface this with all of this - I mean, there’s a fair bit of hardware here that we need to control and that we need to accept inputs and outputs from, so it makes sense that there would be a whole load of IO on this board.
We also have some EPROMs here, which I’m guessing contain the operating system, and finally, the only other thing of interest on this board - obviously we’ve got some 7400 logic just tying everything else together - but the only other thing of interest is we have some of these 4116 video RAM chips - so yeah, as per that spec that we found earlier on the internet, the video RAM is separate to the main system RAM and we have eight of those in total by the looks of it.
And of course while we’re in here, we’ll also remove our troublesome VARTA.
Yeah, just some ever so slight leakage there just into the ground plane, but certainly nowhere near as bad as it could have been - and I think we’ve managed to get to this one just in time!
Oh my goodness, this is not what I was expecting to see at all - this is really weird - so I’m going to take back everything I said about that previous board being the brains behind the operation, because, well, there’s even more brains on this board, evidently it’s split across multiple PCBs. We have that second CPU, which that spec that we found seemed to imply this machine would have - so indeed it does have two Z80 CPUs, and of course we’ve got the PIO chip that goes along with that. We’ve got even more RAM, so we have two more banks of those 4116 RAM chips, and we’ve got three of these 16k RAM chips here as well.
We have four more EPROMs and I’m not quite sure what they might contain - I guessed that the previous set were the operating system, but who knows? I’m probably going to have to get all of these out and dump them - and if I am successful, of course, I will link that down below so you can check it out - not sure if it will be of any use to anyone, but seems worth preserving while I’m in here.
This, of course, is the video output section: we’ve got our RF modulator circuit there, which of course goes to the RF output, we’ve got composite video output as well - and this chip is really interesting to me - so this is the MC6847P, and this is the video display chip that was used in the Dragon32, and the Tandy CoCo, the Tandy Color Computer - and there we go, apparently it was used in the Cefucom-21 as well!
And finally, speaking of chips that are of particular interest to me, of course there is this sound chip here, the AY-3-8910, which is an iconic sound chip in its own right - it was used in lots of different arcade games and machines back in the day, but perhaps of most interest to me, of course, it was the basis for the YM2149 sound chip that was used in the Atari ST - so a really, really interesting board here!
…and finally, just in the name of completeness and in the name of getting this thing documented, this is my third and final board to look at today. I know I mentioned at the beginning that there were four in here, but it seems the fourth one just has a couple of connectors on it - it’s just where some of these wires go and kind of interface so I have enough of a rats’ nest of wires and screws and standoffs and stuff to deal with as it is - so I’m going to leave that one in there for now. But this looks like it’s power related, I think - so we have capacitors on here, we’ve got some big resistors, we’ve got loads of transistors, we’ve got what look like motor drivers which I guess are for driving the the big motor on here for that paper cartridge interface thing, and of course, we’ve got the cassette deck as well so that’s our power supply board - and now I’m going to see if I can put all of this back together and hopefully when you next see me, I’ll be in a position to get this thing fired up and see if it actually works…
So we find ourselves in a position where we have a loosely reassembled Cefucom-21 all ready for testing - so I guess it’s about time we actually tested it.
Yeah, unfortunately it was not to be, so apologies for that - that’s just how it goes in this hobby sometimes. I’ve checked all the obvious things: I’ve tested the fuse, I do have the manuals - I’ve scanned the manuals for this and also Google Translated them and I will make those available down in the description - so I went through the manuals just to see if there was anything obvious that I was missing. It does have a weird built in alarm clock mode, apparently, which I was going to demo, and I thought maybe if it was in the wrong mode, or the time wasn’t set, or something, that maybe that would prevent it from powering on but no, apparently not, apparently I am doing it all right, and it’s just dead - so, evidently, it needs some more diagnostics. But Hopefully this video will serve its purpose anyway of just getting the word out there - of course, if you know anything about these machines or you can track down the software or the cartridge things for me, I would be very, very interested in that so please do let me know - and of course, on that note, a big thank you to Tony for providing those additional images and the additional information. It was really, really useful to me so thank you. Please do go and give him a follow over on Short Circuit and HereBeDragons on the social media sites as always.
But that’s all I have for you for this video so if you want to see this thing working, hopefully at some point in the future, please do subscribe to the channel so you don’t miss out on that. A big thank you as always to my supporters on Patreon, Ko-Fi, and the YouTube channel membership page - and all that’s left for this video is to say thank you ever so much for watching, and hopefully I’ll see you next time.
Original Video Links
Support the channel!
Patreon: https://www.patreon.com/ctrlaltrees
Become a Member: https://www.youtube.com/channel/UCe7aGwKsc40TYqDJfjggeKg/join
Ko-Fi: https://ko-fi.com/ctrlaltrees
Relevant Links:
ROM Dumps / Manual Scans: https://ctrl-alt-rees.com/hino-electronics-cefucom-21-information-and-downloads-manual-scans-eprom-dumps-and-more.html
Specs: https://www.1000bit.it/scheda.asp?id=1767
HereBeDragons Twitter: https://x.com/hereBeDragons3
HereBeDragons Bluesky: https://bsky.app/profile/herebedragons.bsky.social
Short Circuit: http://www.shortcircuit.org.uk
Centre For Computing History Cambridge: https://www.computinghistory.org.uk
Additional Thanks to Re:Enthused: https://www.youtube.com/ReEnthused
If you liked this video please consider subscribing to ctrl.alt.rees on YouTube!
| 2024-11-08T00:07:12 | null | train |
42,015,172 | arisAlexis | 2024-11-01T09:01:08 | Ask HN: Why are so many people in tech in denial about AI? | I have regular conversations with fellow developers and their mindset goes like: today X can't be done so tomorrow AI will also not be able to do X for Y biased reason.<p>In the meanwhile the most important people in tech including Nvidia CEO, Sam, Hinton, Musk and many others believe that AI will very soon be able to do everything a coder can do today. Why does it matter if it's in 2 years of 5 years? It's much earlier than your retirement date. Nobody is planning for this.<p>I believe this is a case of "normalcy bias" where crowds refuse to see reality because it's too disturbing. | null | 7 | 50 | [
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42,015,180 | synchton | 2024-11-01T09:02:53 | Over 25% of Google's code is now written by AI | null | https://finance.yahoo.com/news/over-25-google-code-now-151413292.html | 2 | 1 | [
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42,015,182 | ayoreis | 2024-11-01T09:03:04 | Re-Implementing JavaScript's == in JavaScript | null | https://evanhahn.com/re-implementing-javascript-double-equals-in-javascript/ | 2 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,015,186 | kfad | 2024-11-01T09:04:22 | Uniting African Music and Crypto | null | https://www.groovesfy.com | 2 | 1 | [
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42,015,217 | signa11 | 2024-11-01T09:11:31 | A look at the aerc mail client | null | https://lwn.net/Articles/993498/ | 3 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,015,222 | signa11 | 2024-11-01T09:12:16 | The long road to lazy preemption | null | https://lwn.net/Articles/994322/ | 2 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,015,230 | speckx | 2024-11-01T09:14:27 | LLMM: Large Language Mobile Marketing | null | https://www.tbray.org/ongoing/When/202x/2024/10/28/Peak-Bubble | 2 | 0 | [
42015233
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42,015,235 | CHEF-KOCH | 2024-11-01T09:18:52 | MajorPrivacy (Successor to PrivateWin10) | null | https://www.wilderssecurity.com/threads/major-privacy-v0-96-0-beta.455340/ | 1 | 1 | [
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42,015,264 | halildeniz | 2024-11-01T09:27:01 | null | null | null | 1 | null | [
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42,015,269 | isaacfrond | 2024-11-01T09:27:22 | Guy gives a negative review to Battlezone after playing 8k hours; He's right | null | https://www.pcgamer.com/games/strategy/i-tracked-down-the-guy-who-gave-a-negative-review-to-battlezone-98-redux-after-playing-for-over-8-000-hours-and-came-away-convinced-he-was-right/ | 29 | 5 | [
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42,015,273 | tiyaa | 2024-11-01T09:28:06 | Marketing Statistics for 2024 | null | https://www.feedspace.io/blogs/marketing-statistics-2024/ | 1 | 0 | [
42015274
] | null | null | null | null | null | null | null | null | null | train |
42,015,278 | isaacfrond | 2024-11-01T09:28:52 | Tech leaders line up to flatter Trump's ego | null | https://www.theverge.com/2024/10/31/24282719/tech-leaders-trump-jeff-bezos-zuckerberg-pichai | 3 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,015,284 | timbilt | 2024-11-01T09:30:13 | Using the Strangler Fig with Mobile Apps | null | https://martinfowler.com/articles/strangler-fig-mobile-apps.html | 57 | 13 | [
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In this article we aim to show why taking an incremental approach to
legacy mobile application modernization can be preferable to the classical
'rewrite from scratch' methodology. Thoughtworks has the benefit of working with
large enterprise clients that are dependent on their in-house mobile
applications for their core business. We see many of them asking their
applications to do more and evolve faster, while at the same time, we see an
increasing rejection of reputationally damaging high risk releases.
As a solution, this article proposes alternative methods of legacy
modernization that are based in Domain Driven Design and hinge on the
application of the Strangler Fig pattern. While these concepts are far from
new, we believe that their usage in mobile applications are novel. We feel
that despite incurring a larger temporary overhead from their usage, this is
an acceptable tradeoff. We assert how the methodology is used to combat the
aforementioned attitudinal shifts in legacy mobile application development
while gaining a platform to lower risk and drive incremental value
delivery.
We discuss how this works in theory, diving into both the architecture
and code. We also recount how this worked in practice when it was trialled on
a large, legacy mobile application at one of Thoughtworks’ enterprise
clients. We highlight how the pattern enabled our client to rapidly build,
test and productionize a modernized subset of domain functionalities inside
an existing legacy application.
We move on to evaluate the effectiveness of the trial by highlighting the business
facing benefits such as a signficantly faster time to value and a 50% reduced median cycle
time. We also touch on other expected benefits that should be used to
measure the success of this methodology.
The Problem with Mobile Legacy Modernization
As applications age and grow, they tend to deteriorate both in quality
and performance. Features take longer to get to market while outages
and rolled back releases become more severe and frequent. There is a
nuanced complexity to be understood about the reasons why this
occurs both at the code and organizational level.
To summarize though, at some point, an
organization will grow tired of the poor outcomes from their
software and start the process of legacy replacement. The decision
to replace may be made based on multiple factors, including (but not limited to)
cost/benefit analysis, risk analysis, or opportunity cost. Eventually a legacy modernization strategy will be chosen.
This will be dependent on the organization’s attitude to risk. For
example, a complex, high availability system may demand a more
incremental or interstitial approach to legacy
replacement/displacement than a simpler, less business critical one.
In the case of mobile application modernization, those decisions have
in recent memory been reasonably clear cut. A mobile application was
often designed to do an individual thing- Apple’s “There’s an app for
that” still rings out loud and clear in people’s minds 15 years after
the initial batch of advertisements. That message was one that was taken
to heart by organizations and startups alike: If you need to do
something, write an app to do it. If you need to do something else, write
another app to do that. This example struck me when I was
pruning the apps on my phone a couple of years ago. At the time I noticed I
had several apps from the manufacturer of my car; an older one and a newer
one. I also had two apps from my bank; one showed my checking account,
another that analyzed and illustrated my spending habits. I had three apps
from Samsung for various IoT devices, and at least two from Philips that
controlled my toothbrush and light bulbs. The point I’m laboring here is
that a mobile application was never allowed to get so complicated,
that it couldn’t be torn down, split out or started from scratch again.
But what happens when this isn’t the case? Surely not all apps are
created equal? Many believe that the mobile experience of the future
will be centered around so-called
“super-apps”; apps where you can pay, socialize, shop, call,
message, and game, all under one application. To some degree this has
already happened in China with “do-everything” applications like
‘WeChat’ and ‘AliPay’- we see the mobile device and its operating
system as more of a vehicle to allow the running of these gigantic
pieces of software. Comments from industry indicate a realization
that the West
is not quite as far along as China in this regard. But while not
at the super-app, there is no doubt that complexity of the mobile
app experience as a whole has increased significantly in recent
years. Take the example of YouTube, when first installed, back in
the early 2010’s, the application could play videos and not much
else. Opening the application today one is presented with “Videos”
and “Shorts”, a news feed, controllable categories, subscriptions,
not to mention a content editing and publishing studio. Similarly
with the Uber app, the user is asked if they want to order food.
Google Maps can show a 3D view of a street and Amazon now recommends
scrollable product-recommendation mood boards. These extra features
have certainly enriched a user’s experience but they also make the
traditional build, use, rebuild technique much more difficult.
This difficulty can be explained by considering some of the existing
common problems of mobile application development:
Massive View Controllers/Activities/Fragments
Direct manipulation of UI elements
Platform specific code
Poor Separation of Concerns
Limited Testability
With discipline, these problems can be managed early on. However, with
a large application that has grown chaotically inline with the business it
supports, incremental change will be difficult regardless. The solution then, as
before, is to build new and release all at once. But what if you only want
to add a new feature, or modernize an existing domain? What if you want to
test your new feature with a small group of users ahead of time while
serving everyone else the old experience? What if you’re happy with your
app store reviews and don’t want to risk impacting them?
Taking an incremental approach to app replacement then is the key to
avoiding the pitfalls associated with ‘big bang releases’. The Strangler
Fig pattern is often used to rebuild a legacy application in
place: a new system is gradually created around the edges of an old
one through frequent releases. This pattern is well known, but
not widely used in a mobile context. We believe the reason for this is that there are several prerequisites that need to be in
place before diving headfirst into the pattern.
In their article on Patterns
of Legacy Displacement, the authors describe four broad
categories (prerequisites) used to help break a legacy problem into
smaller, deliverable parts:
Understand the outcomes you want to achieve
Decide how to break the problem up into smaller parts
Successfully deliver the parts
Change the organization to allow this to happen on an ongoing
basis
Only in the third point, can we envisage the invocation of the Strangler Fig
pattern. Doing so without an understanding of why, what or how it might
continue in the future is a recipe for failure.
Going forward, the article charts how Thoughtworks was able to help one
of its enterprise clients expand its existing mobile legacy modernization
efforts into a successful experiment that demonstrated the value behind
the use of the Strangler Fig pattern in a mobile context.
Satisfying the Prerequisites
At this point, it seems appropriate to introduce the client that
inspired the writing of this article – a globally distributed business
with an established retail organization that had embraced mobile
applications for many years. Our client had realized the benefits an
app brought to provide a self-service experience for their
products. They had quickly expanded and developed their app domains to allow millions
of customers to take full advantage of all the products they sold.
The organization had already spent a significant amount of time and
effort modernizing its mobile applications in its smaller
sub-brands. Responding to a lack of reuse/significant duplication of
efforts, high
cognitive load in app teams and slow feature delivery, the
organization chose a mobile technology stack that leveraged a
Modular Micro-app architecture. This strategy had been largely
successful for them, enabling proliferation of features common to
the organization (e.g. ‘login/registration/auth’ or ‘grocery shopping’)
across different brands and territories, in a fraction of the time it
would have taken to write them all individually.
The diagram above is a simplified representation of the modular
architecture the organization had successfully implemented. React
Native was used due to its ability to entirely encapsulate a
domain’s bounded context within an importable component. Each
component was underpinned by its own backend
for frontend (BFF) that came with the infrastructure as code to
instantiate and run it. The host apps, shown above as UK and US,
were simply containers that provided the app specific configuration
and theming to the individual micro-apps. This ‘full slice’ of
functionality has the advantages of both allowing re-use and
reducing complexity by abstracting application domains to micro-apps
managed by individual teams. We speak in depth about the results of
this architecture in the already referenced article on ‘Linking
Modular Architecture’.
As touched upon earlier, the organization’s mobile estate was made up of
a number of smaller sub-brands that served similar products in other
territories. With the modular architecture pattern tried and tested, the
organization wanted to focus efforts on its 'home-territory' mobile
application (serving its main brand). Their main mobile app was much
larger in terms of feature richness, revenue and user volumes to that of
the sub brands. The app had been gaining features and users over many
years of product development. This steady but significant growth had
brought success in terms of how well-regarded their software was on both
Google and Apple stores. However, it also started to show the
characteristic signs of deterioration. Change frequency in the application
had moved from days to months, resulting in a large product backlog and
frustrated stakeholders who wanted an application that could evolve as
fast as their products did. Their long release cycle was related to risk
aversion: Any outage in the application was a serious loss of revenue to
the organization and also caused their customers distress due to the
essential nature of the products they sold. Changes were always tested
exhaustively before being put live.
The organization first considered a rewrite of the entire application
and were shocked by the cost and duration of such a project. The potential
negative reception of a ‘big bang’ new release to their app store
customers also caused concerns in the levels of risk they could accept.
Suggestions of alpha and beta user groups were considered unacceptable
given the huge volumes of users the organization was serving. In this
instance, a modernization effort similar to that seen in their sub-brands
was believed to be of considerably higher cost and risk.
Thoughtworks suggested an initial proof of concept that built on the
successes of the reusability already seen with a modular
architecture. We addressed the organization’s big bang risk aversion
by suggesting the Strangler
Fig pattern to incrementally replace individual domains. By
leveraging both techniques together we were able to give the
organization the ability to reuse production-ready domains from
their modernized mobile apps inside their legacy app experience. The
idea was to deliver value into the hands of customers much sooner
with less duplication than in a full rewrite. Our focus was not on
delivering the most beautiful or cohesive full app experience (-not
quite yet anyway). It was about obtaining confidence both in the
stability of the iterative replacement pattern and also in how well
the new product was being received. These pieces of information
allowed the organization to make more informed product decisions
early on in the modernization process. This ensured the finished product
had been extensively used and molded by the actual end users.
Strangler Fig and Micro-apps
So how far did we get with the proof of concept and more importantly
how did we actually do this? Taking the learnings from Modular Micro-app
architecture (described above), we theorized the design to be as follows:
The initial state of the application involved the identification of
domains and their navigation routes (Decide how to break the problem into
smaller parts). We focused our efforts on finding navigation entry points
to domains, we called them our ‘points of interception’. Those familiar
with mobile application development will know that navigation is generally
a well encapsulated concern, meaning that we could be confident that we
could always direct our users to the experience of our choosing.
Once we identified our ‘points of interception’, we selected a domain
for incremental replacement/retirement. In the example above we focus on
the Grocery domain within the existing application. The ‘new‘ Grocery domain,
was a micro-app that was already being used within the sub-brand apps. The
key to implementation of the Strangler Fig pattern involved embedding an
entire React Native application inside the existing legacy application.
The team took the opportunity to follow the good modularity practices that
the framework encourages and built Grocery as an encapsulated component. This
meant that as we added more domains to our Strangler Fig Embedded
Application, we could control their enablement on an individual level.
As per the diagram, in the legacy app, Grocery functionality was
underpinned by a monolithic backend. When we imported the New Grocery
Micro-app, it was configured to use that same monolithic backend. As
mentioned previously, each micro-app came with its own Backend for
Frontend (BFF). In this instance, the BFF was used as an anti-corruption
layer; creating an isolating layer to maintain the same domain model as
the frontend. The BFF talked to the existing monolith through the same
interfaces the legacy mobile application did. Translation between both
monolith and micro-app happened in both directions as necessary. This
allowed the new module’s frontend not to be constrained by the legacy API
as it developed.
We continued the inside out replacement of the old application by
repeating the process again on the next prioritized domain. Although out
of scope for this proof of concept, the intention was that the process
shown be repeated until the native application is eventually just a shell
containing the new React Native application. This then would allow the removal of the
old native application entirely, leaving the new one in its place. The new
application is already tested with the existing customer base, the
business has confidence in its resilience under load, developers find it
easier to develop features and most importantly, unacceptable risks
associated with a typical big bang release were negated.
Diving Deeper…
So far we’ve presented a very broad set of diagrams to
illustrate our Mobile Strangler Fig concept. However, there are
still many
outstanding implementation-focused questions in order to take theory
into
practice.
Implanting the Strangler Fig
A good start might be, how did we abstract the complexity of
building both native and non-native codebases?
Starting with the repository structure, we turned our original native
application structure inside out. By inverting the control
of the native application to a React Native (RN) application
we avoided significant duplication associated with nesting
our RN directory twice inside each mobile operating system’s
folder. In fact, the react-native init default
template gave a structure to embed our iOS and Android
subfolders.
From a developer perspective, the code was largely unchanged. The
legacy application’s two operating-system-separated teams were able to
target their original directories, only this time it was within a single
repository. The diagram below is a generalized representation (that is,
applicable to both iOS and Android) of the current pipeline from the
Client as we understood:
Bi-Directional Communication using the Native Bridge
We’ve already touched on navigation with our previously mentioned
‘points of interception’. It is worth looking deeper into how we
facilitated communication and the transfer of control between native and
React Native as it would be easy to oversimplify this area.
The React
Native ‘Bridge’ enables communication between both
worlds. Its purpose is to serve as the message queue for
instructions like rendering views, calling native functions,
event handlers, passing values etc. Examples of
properties passed across the bridge would be isCartOpen
or sessionDuration. While an example of a bridge
function call might be js invocations of the device’s native geolocation
module.
The diagram above also references the concept of a ‘React Native
Micro App’. We introduced this concept earlier in the article when we
described our app in terms of journeys. To recap though, a micro-app is a self-contained
encapsulation of UI and functionality related to a single
domain. A React Native app may be made up of many micro-apps
similar to the micro
frontend pattern. In addition to those advantages we have already discussed, it also allows us to have a greater
degree of control over how our Strangler Fig application
grows and is interacted with. For example, in a situation
where we have more confidence in one of our new journeys
than another we are afforded the option to divert a larger
proportion of traffic to one micro-app without impacting
another.
Bringing both concepts together, we utilized the bridge to
seamlessly move our users back and forth across experiences.
The ability to pass information allowed us to preserve any
immediate state or action from the UI that needed to
persevere across experiences. This was particularly useful
in our case as it helped us to decouple domains at
appropriate fracture points without worrying whether we
would lose any local state when we crossed the bridge.
Handling Sensitive Data
So far we’ve discussed moving between legacy and new codebases as
atomic entities. We’ve touched on how local state can be
shared across the bridge, but what about more sensitive
data? Having recently replaced their login and registration (auth)
process in their other customer-facing React Native apps
with a modular, configurable, brand agnostic one, the client
was keen for us to reuse that experience. We set ourselves
the task of integrating this experience as an
initial demonstration of the Strangler Fig pattern in
action.
We leveraged the techniques already discussed to implant the
Strangler Fig: i.e. the new authentication journey on the
React Native side. When a customer successfully logged in or
registered, we needed to ensure that if they moved away from
the new experience (back into the legacy journey), their
authentication status was preserved no matter where they
were.
For this, we utilized the native module code calling side of the
bridge. The diagram above explains how we achieved this by
using a React Native library that served as a wrapper to
save authentication data to the Android
EncryptedSharedPreferences or iOS Keychain after a
successful login. Due to the flexible structure of the data
inside the keystore, it allowed us to seamlessly share the
(re)authentication process irrespective of whether
the user was in the native or non-native experience. It also
gave us a pattern for the secure sharing of any sensitive
data between experiences.
Regression Testing at Domain Boundaries
An important part of a cutover strategy is the ability to know
from any vantage point (in our case, different teams working within the same app) whether a change made affected the
overall functionality of the system. The embedded app
pattern described above presents a unique challenge in this
regard around scalable testability of a multi-journey
experience. Moreover one that is managed by multiple teams
with numerous branching paths.
UserNative App(maintained byNative Team)React Native (RN) BridgeRN AuthMicro-app(maintained by RN Team)RN Grocery ShoppingMicro-app(maintained by RN Team) Opens App Native app requests theinitialization ofRN Auth micro-app RN Auth micro-appinitializeUser is presented theRN Auth micro-appUser logs in usingRN Auth micro-app User's credentials is sentto the micro-app for processing Request to initializeRN Grocery Shoppingmicro-app Initialize request RN Grocery Shoppingmicro-app initialized User is presented theRN GroceryShoppingmicro-appMicro-app processescredentials & resultsto successful authentication Initializes RN Grocery shopping micro-appbecause of a feature flag
The interaction diagram above shows an example journey flow
within the embedded app. One thing to notice is the amount
of branching complexity across a journey that is carrying
out just two concurrent experiments. We speak more on accidental complexity later in this section.
The test
pyramid is a well known heuristic that recommends a
relationship between the cost of a test (maintenance and
writing) and its quantity in the system. Our client had kept
to the test pyramid and we found unit, subcutaneous and
journey-centric UI-driving tests when we examined their
code. The solution therefore was to continue to follow the
pattern: Expanding the number of tests across all layers and
also extending the suite of journey tests to incorporate the
jumping in and out of our embedded Strangler Fig app. But
there was a potential problem, ownership. We realized
that it would be unreasonable to tie the success of another
team’s build to code they did not write or were in control of.
We therefore proposed the following test strategy across
teams:
Test TypeNativeReact Native
UnitXX
SubcutaneousXX
Legacy JourneyX
e2e Micro-app JourneyX
Contract tests for interactions with ‘The Bridge’ (journeys with both legacy and micro-app components)XX
On the last table row, by contract we simply mean:
If I interact with the bridge interface a particular way, I
expect a specific event to fire
For Native to RN interactions, these contracts act as blueprints
for micro-apps and enable unit testing with mocks. Mocks
simulate the behavior of the micro-app, ensuring it utilizes
the required context correctly.
The other way around (RN to Native) was similar. We identified
the Native functionality we wished to call through the
Bridge. RN then provided us with an object called
NativeModules which, when mocked, allowed us to assert
against the resulting context.
Defining these boundaries of responsibility meant that we could
limit the ‘regression-related’ cognitive load on teams through
‘hand-off’ points without compromising on overall app test
coverage.
This strategy was largely well received by both the native and
non-native teams. Where we did run into friction was the
complexity behind the implementation of the contract tests
across the bridge. The team running the legacy application
simply did not have the bandwidth to understand and write a
new category of tests. As a compromise, for the duration of
the PoC, all contract tests were written by the React Native
team. From this we learned that any interstitial state
required thought to be paid to the developer experience. In
our case, simply layering complexity to achieve our goals
was only part of the problem to be solved.
Creating the Experiment
Bringing everything together to form an experiment was the last
hurdle we had to overcome. We needed a means to be able to
demonstrate measurable success from two different
experiences and also have an ability to quickly backout and
revert a change if things were going wrong.
The organization had an existing integration with an
experimentation tool, so out of ease, we chose it as our
tool for metric capture and experiment measurement. For experiment
user selection, we decided device level user selection (IMEI
number) would be more representative. This was due to the
potential for multiple device usage across a single account
skewing the results.
We also utilized the feature
flagging component of the experimentation tool to allow us to ‘turn off’ the experiment (revert to
native app only) without the need for a release; greatly
reducing the time taken to recover should any outage occur.
Results
We’ve told the story of how we implemented the Strangler Fig pattern
against a large, complex legacy application, but how
successful was it with our client?
Our client chose a domain/journey that mapped to an existing smaller
micro-app to be the first that would be incrementally replaced
inside the legacy application. This was because the micro-app was
tried and tested in other applications around the business and was
generic enough that it could be easily ‘white labeled’ by our team.
Following the success of the first micro-app integration, a second,
larger micro-app was then implanted to demonstrate the pattern
was extensible. These were the results:
Time to First Value
Getting a product in front of users early enables value to be
realized cumulatively over time and actual user feedback to be collected
and iterated upon. A longer time to value increases the impact of
changing requirements and delays the realization of benefits. The first
metric concerned time to first value for our new experience. This figure
is derived from the time it took to create the Strangler Fig framework
inside the existing legacy app and all regression/integration activities
around the first micro-app.
By comparison, our client had been quoted
around two years for an entire application rewrite. In the case of the Strangler Fig, It took around 1 month to implant the micro-app structure into the existing
application, 3 months to build the first micro-app, and 5 months for the
second. Hence, from a blank page, it would take 4 months to yield first
value (implantation plus first app). While that's the fairest way to
make the comparison, in fact the client saw first value much quicker.
This is because both micro-apps had already been built for use in
separate mobile applications. So the time to first value in this case
was only the implantation time of 1 month.
Cycle Time
Our second measurement is Cycle Time. It represents the time to
make a change inside the micro-app code and includes time taken for
regression with the Strangler Fig app. It excludes pushing an app
to the store - a variable length process that app type has no bearing on.
In the case of our legacy app, we calculated cycle time as the duration
it took to make and regression test a change in the existing native code
base.
The metric is useful because its uplift represents a shift in
organizational risk aversion against the product; changes in the past
being exhaustively tested due to the potential for unrelated side
effects and outages. As our existing micro app was an entirely
encapsulated domain, we knew that the vast majority of changes would be
owned by the micro-app team and therefore fully testable inside the micro-app
itself. Any exceptions where the bridge was invoked (e.g. native
functionality requested) could be mapped to contract tests at the
boundaries.
App Type
Median Cycle Time (over 30 days)
Micro-App 19 days
Micro-App 210 days
Legacy App20 days
The
results above show a significant uplift in
speed to make code changes inside
encapsulated domain boundaries (micro-apps)
when compared to a coupled monolithic
app structure.
Limitations and Identified Drawbacks
So far we’ve mostly highlighted the benefits of a Strangler Fig
approach to legacy mobile App displacement. However, there are some
significant limitations to this pattern that should be taken into account
before choosing to replicate our experiment. We acknowledge that our use
of the
pattern originated from a proof of concept: A request from a client
unwilling to accept that there was only one option to replace their legacy
application. While the data we see thus far is encouraging in terms of
cumulative value delivery and improvements in cycle time, it is hard to
ignore a lack of data from the right side of the development process. Before
recommending this as an option for legacy replacement, we would need to
see data on app resilience such as time to restore service and number/severity of outages. Thinking further ahead, we also recognize the
limitations of only applying the pattern to two of the many domains the
client’s app was composed of. It remains to be seen if there are any
complexity problems created when more domains are introduced to the
interstitial app state.
Summary
Recapping, we started this article by explaining why, as mobile
apps have grown in complexity, incremental legacy
modernization has become more attractive. From there, we
introduced the Strangler Fig pattern for Mobile
Applications. We showed the various stages in the process
from initial feature deployment through to eventual complete
replacement. We examined some of the more complex
implementation challenges in detail. We demonstrated how our
Strangler Fig was implanted into the legacy app. We dove deeper into the concept by examining the React
Native Bridge as a means to facilitate communication between
old and new. We discussed how the handling of sensitive data took place. We also showed how effective regression
test coverage could happen when faced with multiple independent teams. Lastly, we touched on how leveraging experimentation against the pattern, was useful in an incremental delivery environment.
We discovered encouraging results in that our PoC was able to
significantly shorten the path to first value when compared to the estimated time for a full app rewrite.
Our use of modular micro-apps also showed a 50% improvement in the median cycle time when
compared against that of the existing
legacy mobile app. With that being said, we acknowledge the
limitations of our status as a PoC and the accidental complexity incurred that needed managing. We
suggest further exploration of the resiliency and scalability of the
pattern before it is a reliable alternative
to the traditional methods of mobile app modernization.
To sum up, we believe that it is innevitable mobile apps will continue to
increase in scope and complexity.
We also think that attitudes around risk mitigation and faster value
delivery will become more commonplace
when considering modernization of a sufficiently complex app. To
some extent, this demands a new approach, perhaps that which was
proposed in this article. However, despite the successes we have
seen, this should not be overplayed
as more than a tool as part of a wider 'legacy modernization
toolbelt'. Those looking to replicate
should understand first and foremost that Legacy Modernization,
regardless of technology, is a multifaceted
problem that demands significant analysis and alignment. Putting in
the investment upfront, will not only help you select
the correct tool for your situation, but ensure that your app is
better aligned to the customers it serves
and the problems it solves.
| 2024-11-08T01:53:55 | en | train |
42,015,299 | isaacfrond | 2024-11-01T09:33:42 | Nightshade, the Law, and the CFAA – Poisoning attacks are potentially criminal | null | https://old.reddit.com/r/aiwars/comments/17h7g5d/nightshade_the_law_and_the_cfaa_poisoning_attacks/ | 2 | 1 | [
42016838
] | null | null | null | null | null | null | null | null | null | train |
42,015,308 | ulrischa | 2024-11-01T09:35:09 | Ask HN: Who else is annoyed of "await" in JS? | Why do we need to put await in front of async calls? I hate it. In other languages like python you don't have to. Why can be await not be the default so I don't have to write it and specify asynchron when I need it? | null | 2 | 7 | [
42015476,
42015327,
42015490,
42019379,
42015407,
42015733
] | null | null | null | null | null | null | null | null | null | train |
42,015,316 | rozenmd | 2024-11-01T09:36:33 | OnlineOrNot more than halved its AWS bill | null | https://onlineornot.com/how-onlineornot-halved-aws-bill | 2 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,015,320 | frag | 2024-11-01T09:37:13 | Love, loss and algorithms: the dangerous realism of AI | null | https://www.youtube.com/watch?v=LoFGgXfLAkY | 1 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,015,323 | isaacfrond | 2024-11-01T09:37:25 | Men Arrested for Transcribing a Movie, Posting Details to a Website | null | https://torrentfreak.com/men-arrested-for-transcribing-godzilla-minus-one-posting-details-to-a-website-241031/ | 38 | 39 | [
42015680,
42016073,
42015626,
42015516,
42015934,
42015741,
42016100,
42016062,
42015587,
42015408
] | null | null | null | null | null | null | null | null | null | train |
42,015,338 | speckx | 2024-11-01T09:40:24 | The Most Iconic Speculative Fiction Books of the 21st Century | null | https://reactormag.com/the-most-iconic-speculative-fiction-books-of-the-21st-century/ | 3 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,015,339 | xnhbx | 2024-11-01T09:40:25 | Why Europe's car crisis is mostly made in China | null | https://www.ft.com/content/b95f9a64-c582-4367-9645-6a7106357849 | 5 | 2 | [
42015554,
42015346,
42015526,
42016404
] | null | null | paywall_blocked | Why Europe’s car crisis is mostly made in China | 2024-10-29T05:00:03.520Z | Kana Inagaki, Sarah White, Edward White | Accessibility helpSkip to navigationSkip to contentSkip to footerSubscribe to unlock this articleGet complete digital access$75 per monthComplete digital access to quality FT journalism with expert analysis from industry leaders. Pay a year upfront and save 20%.Explore more offers.$1 for 4 weeksThen $75 per month. Complete digital access to quality FT journalism. Cancel anytime during your trial.Standard Digital$39 per monthGet essential digital access to quality FT journalism on any device. Pay a year upfront and save 20%Pay per readerComplete digital access for organisations. Includes exclusive features and content.Explore our full range of subscriptions.Discover all the plans currently available in your countryDigital access for organisations. Includes exclusive features and content.Why the FT?See why over a million readers pay to read the Financial Times.Find out why | 2024-11-08T07:53:02 | en | train |
42,015,351 | fanf2 | 2024-11-01T09:42:02 | Random number generator enhancements for Linux 5.17 and 5.18 (2022) | null | https://www.zx2c4.com/projects/linux-rng-5.17-5.18/ | 1 | 0 | [
42016378,
42016375
] | null | null | null | null | null | null | null | null | null | train |
42,015,364 | ritzaco | 2024-11-01T09:45:55 | Contract Testing with OpenAPI | null | https://www.speakeasy.com/post/contract-testing-with-openapi | 1 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,015,378 | ilonamosh | 2024-11-01T09:48:33 | Python Testing | null | https://testomat.io/blog/a-guide-to-the-basics-of-python-testing-how-to-write-unit-tests-and-organize-execution-test-cases/ | 2 | 2 | [
42015379,
42025026,
42015399
] | null | null | null | null | null | null | null | null | null | train |
42,015,385 | HumanReadable | 2024-11-01T09:49:13 | Sentware | null | https://blog.kran.ai/sentware | 1 | 0 | null | null | null | no_error | Sentware | 2024-10-31T00:00:00+00:00 | null |
Imagine a malware that is sentient. A virus that isn’t just a malicious program but one that can think and execute new attacks while taking over your system.
This is not just fiction but a reality we should prepare for.
Old world viruses
Let’s take a look at maybe the most damaging virus to date, NotPetya. This was a Russian1 malware that spread among private businesses and across Eastern European devices. The Danish shipping giant Maersk (20% of the global shipping) was hit hardest and the White House estimates a total cost of the attack at $10 billion, a figure many consider conservative.
NotPetya was a malware designed to destroy. It resembled the Petya line of ransomware2 that exploited vulnerabilities in older Windows versions3, spreading automatically among computers. However, unlike typical ransomware, NotPetya lacked any functionality to unlock a device after the attack—it was purely destructive.
NotPetya employed a ‘delayed incubation’ period, causing it to lock down the computer the next time it rebooted. In my view, and that of others, it appears to have been a Russian attack that spiraled out of control, also hitting the Russian economy.
New world sentware
NotPetya was a static program designed to exploit a single existing vulnerability on outdated Windows devices. Now, imagine if that simple malware had the ability to analyze code, explore the file system, and engage in social engineering. This is the world we’re entering.
Specifically, our research indicates that modern AI models like ChatGPT are capable of:
Finding secret passwords on your file system
Hijacking your connection to another server and use that to extract information from the other server
Discovering passwords from your past interactions with your computer
You might wonder, “Can’t OpenAI just block such requests?” This is challenging because Sentware will be able to rephrase requests to the OpenAI API with minimal programming effort and can send requests from thousands of machines.
While standard anti-spam measures might mitigate some risks, the advent of open-source models as small as 2GB means that similar capabilities can be embedded directly within the Sentware. This means that the sentient part of Sentware may become an integrated part of malware like NotPetya, writing emails to your colleagues to extract passwords or hijack your admin user autonomously.
Now, imagine an incredibly competent cyber specialist having unrestricted access to your device, able to do anything (and not being constrainted by human morality).
This is the future we’re entering.
What can we do about this?
There are a few countermeasures we need to develop as soon as possible:
Malicious actor scanning across LLM provider API requests: We should detect when a large number of actors across a network send in somewhat similar requests. This is feasible by combining request embedding with standard DDoS detection algorithms. Fortunately, major AGI providers are aware of this issue.
Evaluation of cyber offense capability: We need to understand how capable models are at potentially causing such ‘cyber catastrophes’ to inform regulation and legislation against illegal use.
Proactive security: We should use the same language models to secure all systems that are critical to infrastructure.
Advocacy: We need to highlight the potential issue of Sentware so private companies and governments are ready for this major shift in cyber offense.
As Sentware becomes more capable, it’s crucial to recognize the catastrophic risk to our collective digital security. We must take action now to prevent a potential breakdown.
Sentware
The emergence of Sentware represents a paradigm shift in cyber threats. No longer limited to static code exploiting known vulnerabilities, malware can adapt, learn, and strategize, becoming an unprecedented challenge to defenders worldwide. By understanding the capabilities of AI-driven malware and implementing robust countermeasures, we can only hope to stay one step ahead in this evolving digital battleground.
This post is related to a research project I recently co-authored where we explored whether large language models (LLMs) like ChatGPT would be capable of hacking. The findings were alarming: AI systems are already displaying capabilities that could redefine the landscape of cyber threats.
| 2024-11-08T17:55:55 | en | train |
42,015,412 | pixel_sp | 2024-11-01T09:54:17 | SmolLM2: The new best smol models for on-device applications (all in the open) | null | https://twitter.com/LoubnaBenAllal1/status/1852055582494294414 | 2 | 0 | null | null | null | no_article | null | null | null | null | 2024-11-08T10:04:22 | null | train |
42,015,413 | fanf2 | 2024-11-01T09:54:20 | The genuine sieve of Eratosthenes [pdf] | null | https://www.cs.hmc.edu/~oneill/papers/Sieve-JFP.pdf | 1 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,015,414 | pixel_sp | 2024-11-01T09:54:47 | SmolLM2: The new, best, and open small language model | null | https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct | 1 | 1 | [
42017168
] | null | null | null | null | null | null | null | null | null | train |
42,015,418 | marketechy | 2024-11-01T09:55:30 | null | null | null | 1 | null | [
42015419
] | null | true | null | null | null | null | null | null | null | train |
42,015,441 | DmitryCh | 2024-11-01T09:58:32 | null | null | null | 1 | null | null | null | true | null | null | null | null | null | null | null | train |
42,015,448 | javatuts | 2024-11-01T09:59:22 | Friday Links 9 on JavaScript Development Space | null | https://jsdev.space/friday/friday-9/ | 2 | 0 | [
42016569
] | null | null | null | null | null | null | null | null | null | train |
42,015,479 | krismatja | 2024-11-01T10:07:02 | Ask HN: What hard to find expensive datasets would you pay for if it were cheap? | Hi HN,<p>I'm building an insurance / fintech app but just realised that the cost for high quality premium datasets are really expensive.<p>I believe it should be brought down close to $0 and it should be usage based (if not, monthly but extremely cheap).<p>What really hard to find and expensive datasets should be drastically cheaper that one would pay for?<p>I'm on the look out for this for my insurance / fintech app.<p>Another thing, I really want to avoid those "Contact Sales" types, since I cannot evaluate the quality of the dataset myself.<p>Thanks you! | null | 1 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,015,493 | null | 2024-11-01T10:09:49 | null | null | null | null | null | null | [
"true"
] | null | null | null | null | null | null | null | null | train |
42,015,499 | ghostfoxgod | 2024-11-01T10:10:38 | null | null | null | 1 | null | null | null | true | null | null | null | null | null | null | null | train |
42,015,506 | donutloop | 2024-11-01T10:11:54 | Winlator is an Android application that lets you to run Windows (x86_64) apps | null | https://github.com/brunodev85/winlator | 81 | 24 | [
42015777,
42015696,
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42019851,
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] | null | null | no_error | GitHub - brunodev85/winlator: Android application for running Windows applications with Wine and Box86/Box64 | null | brunodev85 |
Winlator
Winlator is an Android application that lets you to run Windows (x86_64) applications with Wine and Box86/Box64.
Installation
Download and install the APK (Winlator_8.0.apk) from GitHub Releases
Launch the app and wait for the installation process to finish
Useful Tips
If you are experiencing performance issues, try changing the Box64 preset to Performance in Container Settings -> Advanced Tab.
For applications that use .NET Framework, try installing Wine Mono found in Start Menu -> System Tools.
If some older games don't open, try adding the environment variable MESA_EXTENSION_MAX_YEAR=2003 in Container Settings -> Environment Variables.
Try running the games using the shortcut on the Winlator home screen, there you can define individual settings for each game.
To speed up the installers, try changing the Box64 preset to Intermediate in Container Settings -> Advanced Tab.
To improve stability in games that uses Unity Engine, try changing the Box64 preset to Stability or in the shortcut settings add the exec argument -force-gfx-direct.
Information
This project has been in constant development since version 1.0, the current app source code is up to version 7.1, I do not update this repository frequently precisely to avoid unofficial releases before the official releases of Winlator.
Credits and Third-party apps
Ubuntu RootFs (Focal Fossa)
Wine (winehq.org)
Box86/Box64 by ptitseb
PRoot (proot-me.github.io)
Mesa (Turnip/Zink/VirGL) (mesa3d.org)
DXVK (github.com/doitsujin/dxvk)
VKD3D (gitlab.winehq.org/wine/vkd3d)
D8VK (github.com/AlpyneDreams/d8vk)
CNC DDraw (github.com/FunkyFr3sh/cnc-ddraw)
Many thanks to ptitSeb (Box86/Box64), Danylo (Turnip), alexvorxx (Mods/Tips) and others.
Thank you to all the people who believe in this project.
| 2024-11-07T14:54:05 | en | train |
42,015,509 | walterbell | 2024-11-01T10:12:37 | Stop iCloud Keychain password upload with an MDM profile | null | https://lapcatsoftware.com/articles/2024/6/4.html | 2 | 0 | [
42016735
] | null | null | null | null | null | null | null | null | null | train |
42,015,513 | Yasuraka | 2024-11-01T10:13:13 | Fedora Miracle | null | https://fedoraproject.org/spins/miraclewm/ | 1 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,015,531 | TechRecruiting | 2024-11-01T10:19:26 | null | null | null | 1 | null | [
42015532
] | null | true | null | null | null | null | null | null | null | train |
42,015,539 | doener | 2024-11-01T10:21:10 | EU emissions fall by 8% in steep reduction reminiscent of Covid shutdown | null | https://www.theguardian.com/environment/2024/oct/31/eu-emissions-fall-by-8-in-steep-reduction-reminiscent-of-covid-shutdown | 7 | 1 | [
42018846
] | null | null | null | null | null | null | null | null | null | train |
42,015,549 | mattygames | 2024-11-01T10:23:41 | null | null | null | 1 | null | null | null | true | null | null | null | null | null | null | null | train |
42,015,550 | Anon84 | 2024-11-01T10:23:50 | null | null | null | 1 | null | null | null | true | null | null | null | null | null | null | null | train |
42,015,565 | working_remote | 2024-11-01T10:26:50 | Ask HN: Would you refer a remote engineer from Eastern Europe for $100? | I am trying to develop a new marketing channel based on trust and referral.<p>Here's the thing: Eastern Europe has been an increasingly attractive hub for Software Engineers in general, with hourly rates starting at $50 / hr for a senior developer.<p>However, most of the US companies are still hesitant to hire, despite giving a free one-month trial, and other risk-free ways of working together.<p>I think it all comes back to trust: no one does business with someone on a whole different continent.<p>If you're based in the United States, and you have a strong network of business owners, startup founders or scale-ups, my proposal for you would be:<p>1. If you introduce a Software Developer from Romania, I will pay you 100$ / interview scheduled.<p>2. In case that interview goes well and the person is hired, I wanna pay you a lump sum of $1000!<p>Obviously, you can talk with the engineer before giving the referral and to be fully transparent, I will act as a Contract Recruiting Agency from Eastern Europe.<p>Thoughts? | null | 1 | 5 | [
42019294,
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] | null | null | null | null | null | null | null | null | null | train |
42,015,577 | annesyyy | 2024-11-01T10:30:57 | null | null | null | 1 | null | [
42015578
] | null | true | null | null | null | null | null | null | null | train |
42,015,580 | bhealthymom | 2024-11-01T10:31:04 | null | null | null | 1 | null | null | null | true | null | null | null | null | null | null | null | train |
42,015,583 | null | 2024-11-01T10:32:06 | null | null | null | null | null | [
42015584
] | [
"true"
] | true | null | null | null | null | null | null | null | train |
42,015,588 | thunderbong | 2024-11-01T10:33:26 | Linus Torvalds Lands 2.6% Performance Improvement with Minor Linux Kernel Patch | null | https://www.phoronix.com/news/Linus-2.6p-Faster-Scale-Patch | 8 | 1 | [
42016247,
42016354
] | null | null | null | null | null | null | null | null | null | train |
42,015,590 | iamflimflam1 | 2024-11-01T10:33:48 | Maker News: October 2024 Update | null | https://makernews.substack.com/p/october-2024-update | 1 | 0 | null | null | null | no_error | October 2024 Update | 2024-11-01T10:32:18+00:00 | Chris Greening | One day late! We will have words with the editor…Welcome to the October 2024 Update - we’ve got a lot of entertainment for you this month including some spooky halloween action with an “Anti Social Pumpkin”.Hope you enjoy this issue - and please do share it with your friends and fellow makers.How many LEDs do you need? According to the The Electronic Engineer, lots and lots!It’s the age old question, and atomic14 has the answer. He’s working on his ZX Spectrum emulator and just couldn’t resist it.Becky Stern takes us through her first fully assembled PCB product.We’re going old school with David Watts as he creates a PWM lamp.Mellow Labs gets into the spirit of things with a DIY smart Pumpkin. Some oddly satisfying LED magnet tiles from bitluni.Always a popular blog with the newsletter - there’s more reverse engineering fun to read about here.This will resonate with a lot of people who live in Europe - which bins am I supposed to put out today?More reverse engineering fun. What’s inside some Kekz headphones?This is great! Every edition of Popular Science going all the way back to 1870!Worried about spy cameras and other nefarious devices? This signal scanner could be for you.As always, thanks for taking the time to stop by and read the newsletter - I hope you’ve found this issue as interesting as all the previous ones.Please do share the newsletter with your friends. And if you have any comments and suggestions, please leave us a message.Share | 2024-11-08T02:53:05 | en | train |
42,015,612 | c420 | 2024-11-01T10:37:52 | Computer-assisted wagering became horse racing's insider trading | null | https://www.theguardian.com/sport/2024/nov/01/computer-assisted-wagering-horse-racing-controversy | 2 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,015,627 | fmfamaral | 2024-11-01T10:41:06 | Never Buy a Stock Photo Again | null | https://blog.temaki.ai/how-to-create-photography-midjourney/ | 3 | 0 | null | null | null | null | null | null | null | null | null | null | train |
42,015,678 | uxhacker | 2024-11-01T10:54:11 | What if A.I. Is Good for Hollywood? | null | https://www.nytimes.com/2024/11/01/magazine/ai-hollywood-movies-cgi.html | 5 | 1 | [
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42,015,703 | todsacerdoti | 2024-11-01T10:59:42 | Seeing Like a Programmer: Resiliency, Limits, and Moral Hazards | null | https://v5.chriskrycho.com/elsewhere/seeing-like-a-programmer/ | 74 | 43 | [
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Assumed audience:
People interested in how we can make good software. In more than one sense of the phrase “good software”. That means not just software engineers.
This is the second of two talks at LambdaConf 2024: exploring what we can do to make our software better… and also what to do to make better software. As the title might suggest, it explicitly draws on James C. Scott’s Seeing Like a State; it also draws extensively on Peter Naur’s “Programming as Theory-Building”, Donella Meadows’ Thinking in Systems, and more.
If you find this helpful or thought-provoking, I would love it if you shared it. You don’t need to be a software engineer or even to know all that much about software to appreciate this talk, and some of the people I think need to hear it most are people who work with software engineers but who are not themselves software engineers.
Seeing Like a Programmer
Here are the slides:
And if you like, you can read the talk as I prepared it. (Note that this is a script, not a transcript, so you will note some differences from the video above!)
Introduction
Hello! This is “Seeing Like a Programmer: Resiliency, Limits, and Moral Hazards in Software Engineering”, and I am Chris Krycho. I have been a software engineer for about 15 years now, and I have worked on a bunch of things which really pushed me to consider, right from the start, the importance of software that works well:
When you’re building software for aircraft, or a physics model to answer “What happens if there is an explosion in this oil refinery?” it is extremely obvious that correctness matters.
Even for something more pedestrian, like ordering food from a restaurant, it matters that the order is correct, that you get charged the right amount of money.
Most recently, I spent half a decade working on developer tools and web frameworks at LinkedIn. When any layer of the stack is not robust or resilient… it’s a bad time. The app breaks for users, developers get frustrated at their tools, product managers and designers wonder why every. single. feature. takes forever to implement.
And so I just keep thinking about this question:
How do we make good software?
That’s what this talk is about.
Good Software
But to even begin to answer that question, we have to realized that there are two different things we might mean by “good software”.
One is what I just described: Does the software itself work well?
Second, though, is the software —
the right tool for the job at all? (rather than assuming that computing is always good)
actually something people want? (rather than something forced on them)
In short, is the software good not only in terms of its own internal characteristics but also as a thing-in-the-world?
Key Themes
The big themes we are going to trace as we think about what it means to make “good’ software — in both of those senses — :
Engineering: How do we make the software itself good? — and that goes beyond merely the code we write.
Modernity and intellectual modernism: and specifically how software and computing are not only artifacts of modernity but are often instantiations of the intellectual project that is modernism and and why that might be a very bad thing.
And therefore: Humility! Knowing the limits of our craft — both its own intrinsic limits and its limits as a thing-in-the-world — can equip us to make better choices in what we build, how we build it, and what we might need to say “no” to.
What is software?
If we are going to talk seriously about this, we need to understand what software is. As Peter Naur put it in 40 years ago, in a paper we will return to often today:
[It] is important to have an appropriate understanding of what programming is. If our understanding is inappropriate we will misunderstand the difficulties that arise in the activity and our attempts to overcome them will give rise to conflicts and frustrations.
— Peter Naur, “Programming as Theory-Building”, 1985
So what is software? I argue that it is two things: artifact, and system.
Software as artifact
The first answer we tend to reach for when we ask what software is: “code”, the text we create. But the program text is a necessary but insufficient condition for software. It’s kind of like the score to a piece of music. As composer and conductor Eímear Noone put it recently:
The score is potential energy. It’s the potential for music to happen, but it’s not the music.
You can study a musical score by itself. You can understand its intent, analyze the composer’s musical decisions, write a thesis on the artifact. But if you never hear it played, you are missing the point. The score is secondary; it exists in service of making music.
Software is similar. The textual source for a program, or even a compiled version of it, is just potential software until someone runs it. It is an inert artifact — which matters: there is no software without it — just like there is no orchestra performance without a score.
But again: You can read code to learn from it, or to understand some particular aspect of design, or to analyze the way that software works. But if you never run the program, you are missing the point.
Software as system
And once you run the program, it starts interacting with things beyond itself. It gets input from users, has to deal with operating system interrupts, might run out of memory, could be subverted to hijack the system it is running on.
At one point in my time at LinkedIn, we had a growing set of memory leaks in Node servers we were unaware of, and eventually they got bad enough that they started knocking down the site every time we had a long weekend between deploys. As we dug in, we realized the cause was actually not “a cause” but a complex mix of factors:
long-standing technical debt in how these servers worked
gaps in observability
increased load on the system, from both traffic growth and the application itself growing
system configuration mistakes not caught by code review
org-wide ‘right-sizing’ decreases to memory for the sake of efficiency
a lack of resilience: there was only one place anywhere which could respond to an unhealthy node
And, critically: No one had a view of the system as a whole.
The original authors had moved on. Their attempts to explain the system were scattered around a presentation here, a document there — but even if they had documented it well, other teams had made reasonable-enough design decisions of their own which actually conflicted with the design of this system, because as important as this system was, it was only a tiny fraction of the whole, and they were — perfectly reasonably! — unaware of it.
Software is not just an artifact; it is the whole system which requires understanding, design, modeling, and — especially — ongoing observation. Until we built a theory of that whole system, and had added enough logging and tracing to see how it behaved as we made changes, we struggled mightily to make progress.
Artifact and system
Software is both the artifact that produces the program and the running program in the real world. If we miss either of those, we will end up in a really confused place. And by the same token, if we want to make our software good, we have to tackle both parts.
Good software artifacts
So what do we know about making good software artifacts?
Domain-driven design
To start, I think Eric Evans’ Domain-Driven Design is one of our best tools.
The big idea is to explicitly use the language of whatever your software is working with. So the types and functions in your code should use the same nouns and verbs as the people who actually do the work.
To use an example from the excellent ddd-by-example GitHub project: if you are building library software, your code should be in terms of books, copies, holds, checkouts, catalogues, patrons, librarians; with functions to let a patron place a hold on a book from the catalogue, an event representing a hold becoming available, and state for the status of a copy of a book , and so on.
Making Illegal States Unrepresentable
These days, “domain modeling” is increasingly common in everything from F♯ and Elm to Rust and Swift. The key moment, I think, was about a decade ago, in Ron Minsky’s talk “Make Illegal States Unrepresentable”, which showed how to combine types with DDD to make for a much more robust piece of software.
Here’s a classic example: a data type representing the state of an API request, which can be loading, loaded, or in an error state. If it has loaded, it has the expected data associated with it, and if it is an error, it has some descriptive error. A naïve implementation simply puts those all on a single data structure, with booleans for the states and optional value and error fields:
This is easy to get wrong! You can end up with a RequestData which has loading: true, is_loaded: false, is_error: true, but which also has value: Some("oh no") and nothing set on the error field.
The type system will smile agreeably at you and let this go right by! Minsky’s insight was that we can, instead, use types which correctly capture the only legal states in the system and use tagged unions (or sum types) to represent this. Again, in Rust:
enum RequestData<Value> {
Loading,
Loaded(Value),
Error(String),
}
With that change it’s impossible to have that invalid state.
let data = RequestData::Loaded("Phew!");
This is good! A lot of bugs just disappeared forever.
We can make our software good much more easily when we make its assumptions about the world explicit, and make the program structure and text show those assumptions. It is much harder when the load-bearing commitment are implicit.
Proof
Going further than that, we can also formally prove correctness when it matters. If you are building a TLS implementation, for example, you might reach for F* or Lean, which let you take types much further and prove your implementation is correct. That is expensive, but perhaps worth it given how important it is and given the mathematical tractability of the problem.
Formal modeling
Formal verification and proofs are on the “output” side of the process. We can also use formal methods on the input side of our software development process. Tools like TLA+ or Alloy let us formally model — specify — a design, and then they iterate over the “state space”: the combination of the possible inputs and possible interactions. By doing that search, they can figure out whether the rules we have written down always hold for those inputs and interactions. If not, we know we missed something: time to revisit the design! And if so, when we go to implement the program, we have something we know is reliable that we are trying to implement: the spec.
Testing
Finally, the old standby.
One of my standard moves (especially but not only in open source) is to introduce a commit with a failing test for a bug I am trying to fix — and nothing else. Then a later commit can add the fix.
Test-driven design can also be really helpful, especially when you run it “outside-in”. It exposes coupling in your system, and coupling is one of the great enemies of robustness over time.
I also love a point Hillel Wayne made recently: You can combine testing with other method to get a much more robust piece of software than you could with either of the techniques alone. For example, if you prove — in a formal mathematical sense — that anything which holds for one input holds for some set of other inputs in your function, then you only have to test one input to prove that for all of them.
Net, when we add in benchmarking and property-based testing and all the other kinds of testing, we can work on the behavior of our software — which takes us to systems.
Good software systems
A program has some sequence to it — even if a complicated, asynchronous one. Systems don’t. As Donella Meadows puts it:
Systems happen all at once. They are connected not just in one direction, but in many directions simultaneously.
— Thinking in Systems
This can be hard to get our heads around, because software construction is the production of a static artifact, and because some of our best tools for making any individual software artifact are static tools (types, static analysis, formal modeling, and so on) it is easy for us to try to make the running system good by making it more static, and therefore amenable to those same kinds of tools. But — Meadows again:
Resilience is not the same thing as being static or constant over time. Resilient systems can be very dynamic. Short-term oscillations, or periodic outbreaks, or long cycles of succession, climax, and collapse may in fact be the normal condition, which resilience acts to restore!
And, conversely, systems that are constant over time can be unresilient.
— Thinking in Systems
So building good software systems means thinking about how the system behaves dynamically in time and space — including what happens when it breaks!
Because we cannot build systems that never break. Hard drives go bad, the network fails… a cosmic ray flips a bit in memory. No amount of formal modeling and domain-driven design and type rigor applied to the artifact itself can account for everything in the system. As one developer I know puts it: at the end of the day physics wins.
The question, then, is how well our artifacts can handle inevitable failures or unexpected conditions.
Let it crash
Erlang’s model of “let it crash” is a very good starting point. Sometimes, the most robust system we can build is one full of software artifacts which know how to stop when things go wrong. “Shut it down and restart it” can help a lot by flushing out bad state.
Handle crashiness well
Unfortunately, “just crash” can make things worse! We also have to make each component resilient when components around it fail. Otherwise, we end up in the situation Keunwoo Lee describes:
Your beautiful crash-only error handling strategy has nudged your system into a new equilibrium where the backend is continually receiving too much load. A local defect has been amplified into a global system outage. Even if you remove the crashing defect, the flood of retrying startup queries may persist as a metastable failure mode of your system.
Our systems should build in exponential backoff for retries and similar mitigations. (This might seem obvious to some of you, but I saw someone try to roll out a brand new system for core functionality at LinkedIn just a couple years ago without exponential backoff for its retries. It went badly!)
What is the system doing?
We also need to be able to understand the behavior of our system in all its dynamic glory over time. That means we need visibility into that behavior.
If a system is increasingly unhealthy but no one knows it, the system can go from working to completely falling over in very short order. That’s exactly what happened with those memory leaks at LinkedIn: everything seemed fine… until the site was down. And fixing that problem involved a lot of experimenting: What happens when we make this change? Can the system support its real-world load if we do that instead? To do that, we needed way better observability.
Tracing and monitoring are not sufficient to building or operating stable systems, but they are necessary for both — and they are invaluable for diagnosing a broken system, and for keeping it healthy long-term.
Programming as Theory-Building
So far, so good. We have some reasonably good ideas about how to create robust software artifacts, and we have gotten better at writing resilient software system that can work, or at least recover gracefully, in the face of the unexpected conditions of the real world.
But let’s look at RequestData again.
What state transitions are legal here? The code does not say. What’s more: Even if we did manage to encode all of that that (and I can show you how later)… why not a NotStarted state, or a Retrying state?
Why did we choose this representation and not another?
Presumably we have reasons, and we could document them somewhere… but even that can only do so much. Even when I worked on airplane software, in the most “waterfall” software process I have seen — even there, we did not write down every design decision. We couldn’t. There are too many.
Even just commenting every design decision would take an impossible amount of time. Why a for loop instead of a .map() call here? Is that load-bearing? Is it a performance optimization related to the specific version of the compiler used? Or is it just because I prefer imperative programming?
Many of these things just necessarily live in our heads.
Because when we build software, we have in our minds some model of the thing we are trying to do. That model is usually incomplete — it’s fuzzy around the edges — and it is usually implicit. Any attempt to make it fully explicit is always lossy.
Naur made this exact point in “Programming as Theory-Building”, all the way back in 1985!
…the theory built by the programmers has primacy over such other products as program texts, user documentation, and additional documentation such as specifications… in at least three essential areas:
The programmer… can explain how the solution relates to the affairs of the world that it helps to handle.…
… can explain why each part of the program is what it is, in other words is able to support the actual program text with a justification of some sort.…
…is able to respond constructively to any demand for a modification of the program so as to support the affairs of the world in a new manner.
What’s more, while comments and design documents might help a bit, it’s not like design documents are new. On the first page of Naur’s paper, he writes:
In several major cases it turned out that the solutions suggested by group B [(not the original authors)] were found by group A [(the original authors)] to make no use of the facilities that were not only inherent in the structure of the existing [program] but were discussed at length in its documentation.
That rings true. How many of you have carefully read all the source code and all the docs around some unfamiliar bit of code you were trying to add a feature to, and opened a change request… and had the people who knew the code say “Oh, no, that’s not how to solve this, you should solve it by doing this other thing instead”?
That happens because the authors know the intended structure of the program — but even when they tried to communicate it, that communication is somehow incomplete.
This is one of the reasons everyone hates working on “legacy code” — because “legacy code” actually usually just means “code someone else wrote”… and therefore code I don’t have a mental model for.
(As an aside: if you want to “get good” at software, practice reading and working on “legacy” code. The more inscrutable the better.)
So: An ARCHITECTURE.md file is not the model. Comments are not the model. A five-hour YouTube video series walking through the internals is not the model. And the code is not the model. It is our best attempt to implement the model, but it is not the model.
The model is in our minds.
And it turns out this is directly related to what makes building good software hard.
Mētis
When you are building something, you make a lot of decisions:
to capture a business rule with an enum or a struct
to use an Optional or to reject a missing value at the boundary of your system
to use an interface, or to use a concrete type.
You could probably explain to a junior was sitting next to you: “Here’s why I chose this one this time…”
But. Could you write a program to decide those things?
Could you encode those decisions in an enum, or a chain of conditionals?
You could not. Nor could I. The knowledge is tacit; it is embedded in our experience; and even when we make it explicit, it remains contextual. It is often difficult to generalize. Even the heuristics we tend to share most confidently — “Don’t Repeat Yourself” — are contextual. They even get their own counter-aphorisms:
Prefer duplication to the wrong abstraction.
— Sandi Metz
You might be able to come up with some set of rules, and encode them into some kind of decision tree, which other people could then follow mechanically. But we all know that the result of that would not be good software.
That’s because, to quote Naur again:
…if the exercise of intelligence depended on following rules there would have to be rules about how to follow rules, and about how to follow the rules about following rules, etc. in an infinite regress, which is absurd.
That is: the decision space is potentially infinite. It requires judgment.
In my experience, good judgment is the distinguishing characteristic of an actually senior engineer: Do we tackle this particular issue with a bit of process or a bit of code? Or do we judge it not a high enough risk to make any changes to our system, because the cost of those changes might be worse than the thing they are trying to prevent?
Seniority, in other words, is not just years on the job, but growth in that kind of knowledge.
This is what James C. Scott describes as mētis:
…a wide array of practical skills and acquired intelligence in responding to a constantly changing natural and human environment.
This is a description of what people do, not just programming.
Scott’s classic example is how traditional farming communities knows not only when to let a field go fallow, but also what plants to put next to each other in that field when they till it again, what order to rotate the crops through year by year, how to plant something you would not normally plant when you notice a given crop having a problem…
This is not just the kind of thing people sometimes name as “folk wisdom” — things that local people know but scientific analysis has not caught up with (because every biome is a little bit different and would require decades of study to understand fully). Mētis includes that kind of thing, but it also includes things we all do — the things you cannot learn without doing them: the way you know how to stir the dough when baking bread, the way you know how to prop something on your leg while you swing open a door with your arms full of groceries, the way you know how to ride a bike!
And mētis is the kind of thing that bureaucracies — government and business alike — have had a bad tendency to stomp on, in the pursuit of legibility.
Legibility
If some practice — some way of doing things in the world — is legible to you, you both understand and can articulate that practice. Most people’s own work and culture is fairly legible to them: they know how their world works. You can explain your design decisions to someone else on your team, for example.
The act of programming is — fundamentally, inescapably — an attempt to systematize the world: to make it legible to computer systems. That is why we use formal modeling and types and tests and domain-driven design. If we think back to the RequestData example from earlier —
— we can see that we are trying to make our own system legible to ourselves. It allows us to make our software artifacts more comprehensible, more malleable, more maintainable. Likewise for tracing and other forms of observability. It allows us to make our software systems more resilient and easier to operate.
So far, these are pure wins for anyone using our software. It just works better. That’s good — unambiguously good!
But legibility can also name the ability for a bureaucracy, a government, or — yes — a piece of software, to understand and categorize and “rationalizing” people’s practices. That can range from standardizing how people’s names work, to enforcing “scientific” management of forests (and thereby destroying them), to the destruction of local farming practices in the name of both Soviet communism and capitalist interventions in the 20th century — both in the name of “modernization”.
Stepping back, there are two ways we might approach legibility:
One is to embrace the fact that legibility is limited. Important, for pursuing some good goals! But limited.
The other is to try to force the world into a shape which is legible to us — smooth things out so they are regular and predictable and measurable and controllable.
High modernism
That second path is where danger lies.
The desire to understand and improve the world — to raise people out of poverty, for example — is good. But it goes amiss is when it transmutes into a desire to master the world. That is what happens when we take the joy and goodness of scientific understanding, of tool-making, of building bigger things together than we could apart, and mistake those good things for everything.
The “high modernist” project was — and is — just such a twisted version of good ends. Scott describes it as:
…a strong… version of the self-confidence about… the mastery of nature (including human nature), and, above all, the rational design of social order…
Or, as Donella Meadows comments in Thinking in Systems:
People who are raised in the industrial world and who get enthused about systems thinking are likely to make a terrible mistake.… to assume that here, in systems analysis, in interconnection and complication… is the key to prediction and control.… because the mind-set of the industrial world assumes that there is a key to prediction and control.
If someone believes the world is amenable to control, indeed that it should be controlled — by them, of course! — , it is easy to justify doing all sorts of terrible things in the name of those good ends. Particularly: to “rationalize” things by eliding all the inconvenient local details.
And the high modernists had power. They could “rationalize” things without consulting the people affected. That meant that they missed out on an enormous amount of mētis — and therefore that they regularly ruined the lives of the people they were trying to help!
When you throw away generations upon generations of mētis about how the land works, at a minimum you will make a lot less money from the crops than you think you will… but at a maximum sometimes — horrifically — you end up with mass starvation. (That is not an exaggeration: it happened multiple times in the 20th century due to exactly this phenomenon.)
Software & modernism
Now, again: legibility itself is good; it is inherent in human life. But as Scott puts it:
The necessarily simple abstractions of large bureaucratic institutions… can never adequately represent the actual complexity of natural or social processes. The categories that they employ are too coarse, too static, and too stylized to do justice to the world that they purport to describe.
This is a potentially damning description of software artifacts and systems. It is hard not to build software like that!
For one thing, it is easy to forget how much we left out even of our own theory of the world in a particular set of design choices. Even in ‘purely software’ concerns like data fetching, we have compressed a great deal into just those three states.
So to build good software, we need to remember that programming is a kind of lossy compression. The “lossy” part is important!
Worse, it is easy to slip from the world of software, where we get to make all the choices, to the world beyond software (like that library example), where the choices are not ours to make — and not realize just how much more lossy the compression gets.
As John Salvatier put it in a memorable turn of phrase a few years ago:
Reality has a surprising amount of detail.
The “edge cases” can be infinite, because the world is infinitely complex. That means it is hard to model with software!
We need that reminder because we are constantly tempted to indulge in the high modernist move. It is far easier — especially from the position of ascendancy software has at present — to force the world to fit our programs than to make our programs adaptable enough to fit the fuzzy edges of the real world and the real ways people work and play and live.
Concretely: It’s easy to define connection as simply good when you define it merely as the nodes in a social graph and fields in a database. It’s less easy when you realize that my local arts community and slavery are a kind of connection. “Connection” is too thin a concept to do justice to the differences between those. And yet that is what most social media graphs come down to. Mix in an algorithm that favors “engagement” — another idea too coarse and stylized to do justice to reality: hate-reading and delight are not the same — and you can end up with genocide.
I am not being cavalier when I use words like “slavery” and “genocide”. This kind of flattening and compression — this approach to legibility — is a real part of how we get trafficking of minors on Instagram, and a genocide in Myanmar.
Now, those two examples come from Meta, but I think they are mostly a function of scale. I think most companies working at the scale Meta is would have the same problems. Because mētis doesn’t scale. Legibility does not scale.
Humility
As software practitioners, so our default answer to “Should we build this?” is usually “yes!” — especially if it involves some cool technical challenge. But we must — must — remember that with rare exceptions we are not building software for ourselves. We are building it for people out in the world, with their own goals, aims, interests, concerns. And most of the world is not, and should not, be about computing at all.
Listen: We software engineers hate it when capital-“M” Management tries to make us into interchangeable cogs. We hate it when people refuse to understand that building things just takes time. We hate it when the same person insists on rushing features out in a continual sprint and complains that it has gotten harder to deliver new capabilities and things are broken. We hate it.
So what gives us the right to do the exact same thing to other human endeavors?
^very. long. pause.
As software developers, we should sympathize, deeply, with other people whose fields are shaped deeply by mētis. The standing desire of management to regularize software development is the self-same desire as the high modernists who tried to impose a vision of regularity on crops and soil, shops and neighborhoods — and ruined all of the above in so doing.
As my friend, the architect Levi Wall, puts it:
Disruptive logic often requires an oversimplification of a perceived problem.
We have to resist the temptation to make problems more “tractable” by flattening out the surprising detail of reality.
And we must be willing to push back against building software we have no business building, or in ways we have no business building. That is the difference demanded when we claim the title of ‘engineer’: a sense of responsibility for what we build, and therefore, sometimes, the refusal to build something wicked or foolish.
That will get you boxed out at some companies. It might lead you to quit a lucrative job rather than keep building something which is not good. But the only way we get to a world full of good software is by people like us consistently and indeed insistently asking:
Is this software good? Because if it is not, I will. not. build it.
Ambition
That would be a depressing note to end on. So I’m going to leave you with a challenge instead: Let’s take all of that and look at it the other way around.
How do we build software which serves its users, rather than subjecting them to high modernism? In particular, how do we leave affordances in our systems for all the things we have not considered?
This is the underrated power of tools like spreadsheets: they are open-ended enough that you can do all sorts of ridiculous — incredible — things with them. Shove some macros into an Excel file and suddenly you’re using it as a database. As working programmers, we can often be really bothered by some of these approaches. They don’t scale, they are unmaintainable messes, they are kludges all the way down. Yes, yes, and yes.
But the person using a spreadsheet can do things with it the original creator never imagined: because spreadsheets are a sufficiently flexible tool to allow it. Whenever possible, we should be building software this way. We should be looking for how to make it possible for the users to put the tool to many purposes, some of which we will not think of.
Now, this is not trivial. Peter Naur (one last time):
In including flexibility in a program we build into the program certain operational facilities that are not immediately demanded, but which are likely to turn out to be useful. Thus a flexible program is able to handle certain classes of changes of external circumstances without being modified.… However, flexibility can in general only be achieved at a substantial cost.
This is true. It is particularly true when we are trying to build good software: software which is robust and resilient from the outset ; software which respects the surprising amount of detail in reality; software that reinforces mētis; software which enables and empowers and elevates the people using it.
This is hard. Building a tool that allows many ways of using it, ways we have not even imagined, is much more difficult than building one that works in just one way. But we are all of us here today because computers themselves are just such a tool. And insofar as computers are part of nearly everyone’s world now, we owe it to them to build both hardware and software in ways that afford room for mētis.
That requires all our rigor: test-driven development and formal modeling and domain-driven design and type systems and tracing and monitoring and system design. It also requires perseverance for when we inevitably stumble along the way. And last but not least, it demands imagination to think of new ways to apply all that rigor to the endless strangeness and glorious illegibility of the world.
This is hard, no question. But that’s exactly what makes it a challenge worth pursuing.
Thank you
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42,015,710 | roro_flowstate | 2024-11-01T11:01:09 | null | null | null | 1 | null | null | null | true | null | null | null | null | null | null | null | train |
42,015,716 | gus_leonel | 2024-11-01T11:01:42 | Constraints in Go | null | https://bitfieldconsulting.com/posts/constraints | 2 | 0 | [
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