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Home • Automotive News • This Alberta Startup Sells No-Tech Tractors for Half Price

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Four hun­dred in­quiries from American farm­ers poured in af­ter a sin­gle in­ter­view. Not for a John Deere. Not for a Case IH. For a trac­tor built in Alberta with a re­man­u­fac­tured 1990s diesel en­gine and zero elec­tron­ics.

Ursa Ag, a small Canadian man­u­fac­turer, is as­sem­bling trac­tors pow­ered by 12-valve Cummins en­gines — the same me­chan­i­cally in­jected work­horses that pow­ered com­bines and pickup trucks decades ago — and sell­ing them for roughly half the price of com­pa­ra­ble ma­chines from es­tab­lished brands. The 150-horsepower model starts at $129,900 CAD, about $95,000 USD. The range-top­ping 260-hp ver­sion runs $199,900 CAD, around $146,000.

Try find­ing a sim­i­larly pow­ered John Deere for that money.

Owner Doug Wilson is­n’t pre­tend­ing this is cut­ting-edge tech­nol­ogy. That’s the en­tire point. The 150-hp and 180-hp mod­els use re­man­u­fac­tured 5.9-liter Cummins en­gines, while the 260-hp gets an 8.3-liter unit.

All are fed by Bosch P-pumps — purely me­chan­i­cal fuel in­jec­tion, no ECU, no pro­pri­etary soft­ware hand­shake re­quired. The cabs are sourced ex­ter­nally and stripped to es­sen­tials: an air ride seat, me­chan­i­cally con­nected con­trols, and noth­ing re­sem­bling a touch­screen.

This plays di­rectly into a fight that has been sim­mer­ing for years. John Deere’s right-to-re­pair bat­tles be­came a na­tional story when farm­ers dis­cov­ered they could­n’t fix their own equip­ment with­out dealer-au­tho­rized soft­ware. Lawsuits fol­lowed, then leg­is­la­tion.

Deere even­tu­ally made con­ces­sions, but the dam­age was done. A gen­er­a­tion of farm­ers learned ex­actly how much con­trol they’d sur­ren­dered by buy­ing ma­chines loaded with pro­pri­etary code.

Wilson saw the gap and drove a trac­tor through it. The 12-valve Cummins is ar­guably the most widely un­der­stood diesel en­gine in North America. Every in­de­pen­dent shop, every shade-tree me­chanic with a set of wrenches, every farmer who grew up turn­ing bolts has en­coun­tered one.

Parts sit on shelves in thou­sands of stores. Downtime — the thing that ac­tu­ally costs a farmer money dur­ing plant­ing or har­vest — shrinks dra­mat­i­cally when you don’t need a fac­tory tech­ni­cian with a lap­top to di­ag­nose a fuel de­liv­ery prob­lem.

Ursa Ag’s dealer net­work re­mains tiny, and the com­pany sells di­rect. Wilson ad­mit­ted they haven’t scaled up dis­tri­b­u­tion be­cause they can’t keep shelves stocked as it stands. He says 2026 pro­duc­tion will ex­ceed the com­pa­ny’s en­tire cu­mu­la­tive out­put, which is a bold claim from a small op­er­a­tion, and whether they can ac­tu­ally de­liver is the sin­gle biggest ques­tion hang­ing over this story.

The U.S. mar­ket is where things get in­ter­est­ing. Ursa Ag has no American dis­trib­u­tors yet, though Wilson says that’s likely to change. The eas­i­est an­swer is yes, we can ship to the United States,” he told re­porters.

Those 400 American in­quiries af­ter one Farms.com seg­ment sug­gest the ap­petite is real. Farmers who have been buy­ing 30-year-old equip­ment to avoid mod­ern com­plex­ity now have a new al­ter­na­tive — a ma­chine with fresh sheet metal, a war­ranty, and an en­gine phi­los­o­phy rooted firmly in the past.

There’s a rea­son the used trac­tor mar­ket has been so ro­bust. Plenty of op­er­a­tors looked at a $300,000 ma­chine full of sen­sors and soft­ware and de­cided a well-main­tained older unit was the smarter bet. Ursa Ag is man­u­fac­tur­ing that bet from scratch.

Whether a small Alberta com­pany can scale fast enough to meet de­mand from an en­tire con­ti­nent is an­other mat­ter. The big man­u­fac­tur­ers have sup­ply chains, dealer net­works, and fi­nanc­ing arms that took decades to build. Wilson has re­man­u­fac­tured Cummins en­gines and a value propo­si­tion that res­onates with any­one who has ever waited three days for a dealer tech to show up with a di­ag­nos­tic ca­ble.

The farm equip­ment in­dus­try spent 20 years adding com­plex­ity and cost. Ursa Ag is wa­ger­ing that a sig­nif­i­cant num­ber of farm­ers never wanted any of it.

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🚀 DeepSeek-V4 Preview is of­fi­cially live & open-sourced! Welcome to the era of cost-ef­fec­tive 1M con­text length.

🔹 DeepSeek-V4-Pro: 1.6T to­tal / 49B ac­tive params. Performance ri­val­ing the world’s top closed-source mod­els.

🔹 DeepSeek-V4-Flash: 284B to­tal / 13B ac­tive params. Your fast, ef­fi­cient, and eco­nom­i­cal choice.

Try it now at chat.deepseek.com via Expert Mode / Instant Mode. API is up­dated & avail­able to­day!

📄 Tech Report: https://​hug­ging­face.co/​deepseek-ai/​DeepSeek-V4-Pro/​blob/​main/​DeepSeek_V4.pdf

🤗 Open Weights: https://​hug­ging­face.co/​col­lec­tions/​deepseek-ai/​deepseek-v4

DeepSeek-V4-Pro​

🔹 Enhanced Agentic Capabilities: Open-source SOTA in Agentic Coding bench­marks.

🔹 Rich World Knowledge: Leads all cur­rent open mod­els, trail­ing only Gemini-3.1-Pro.

🔹 World-Class Reasoning: Beats all cur­rent open mod­els in Math/STEM/Coding, ri­val­ing top closed-source mod­els.

DeepSeek-V4-Flash​

🔹 Reasoning ca­pa­bil­i­ties closely ap­proach V4-Pro.

🔹 Performs on par with V4-Pro on sim­ple Agent tasks.

🔹 Smaller pa­ra­me­ter size, faster re­sponse times, and highly cost-ef­fec­tive API pric­ing.

Structural Innovation & Ultra-High Context Efficiency​

🔹 Novel Attention: Token-wise com­pres­sion + DSA (DeepSeek Sparse Attention).

🔹 Peak Efficiency: World-leading long con­text with dras­ti­cally re­duced com­pute & mem­ory costs.

🔹 1M Standard: 1M con­text is now the de­fault across all of­fi­cial DeepSeek ser­vices.

Dedicated Optimizations for Agent Capabilities​

🔹 DeepSeek-V4 is seam­lessly in­te­grated with lead­ing AI agents like Claude Code, OpenClaw & OpenCode.

🔹 Already dri­ving our in-house agen­tic cod­ing at DeepSeek.

The fig­ure be­low show­cases a sam­ple PDF gen­er­ated by DeepSeek-V4-Pro.

API is Available Today!​

🔹 Keep base_url, just up­date model to deepseek-v4-pro or deepseek-v4-flash.

🔹 Supports OpenAI ChatCompletions & Anthropic APIs.

🔹 Both mod­els sup­port 1M con­text & dual modes (Thinking / Non-Thinking): https://​api-docs.deepseek.com/​guides/​think­ing_­mode

⚠️ Note: deepseek-chat & deepseek-rea­soner will be fully re­tired and in­ac­ces­si­ble af­ter Jul 24th, 2026, 15:59 (UTC Time). (Currently rout­ing to deepseek-v4-flash non-think­ing/​think­ing).

🔹 Amid re­cent at­ten­tion, a quick re­minder: please rely only on our of­fi­cial ac­counts for DeepSeek news. Statements from other chan­nels do not re­flect our views.

🔹 Thank you for your con­tin­ued trust. We re­main com­mit­ted to longter­mism, ad­vanc­ing steadily to­ward our ul­ti­mate goal of AGI.

The DeepSeek API uses an API for­mat com­pat­i­ble with OpenAI/Anthropic. By mod­i­fy­ing the con­fig­u­ra­tion, you can use the OpenAI/Anthropic SDK or soft­wares com­pat­i­ble with the OpenAI/Anthropic API to ac­cess the DeepSeek API.

* The model names deepseek-chat and deepseek-rea­soner will be dep­re­cated on 2026/07/24. For com­pat­i­bil­ity, they cor­re­spond to the non-think­ing mode and think­ing mode of deepseek-v4-flash, re­spec­tively.

Invoke The Chat API​

Once you have ob­tained an API key, you can ac­cess the DeepSeek model us­ing the fol­low­ing ex­am­ple scripts in the OpenAI API for­mat. This is a non-stream ex­am­ple, you can set the stream pa­ra­me­ter to true to get stream re­sponse.

For ex­am­ples us­ing the Anthropic API for­mat, please re­fer to Anthropic API.

curl

python

nodejs

curl https://​api.deepseek.com/​chat/​com­ple­tions \ -H “Content-Type: ap­pli­ca­tion/​json” \ -H “Authorization: Bearer ${DEEPSEEK_API_KEY}” \ -d ‘{ “model”: “deepseek-v4-pro”, “messages”: [ {“role”: “system”, “content”: “You are a help­ful as­sis­tant.“}, {“role”: “user”, “content”: “Hello!“} ], “thinking”: {“type”: “enabled”}, “reasoning_effort”: “high”, “stream”: false }’

We’re check­ing if you’re a real per­son and not an au­to­mated bad bot. Usually, the captcha be­low will com­plete it­self. If it does­n’t, sim­ply click the check­box in the captcha to ver­ify. Once ver­i­fied, you’ll be taken to the page you wanted to visit.

If for some rea­son af­ter ver­i­fy­ing the captcha above, you are con­stantly be­ing redi­rected to this ex­act same page to re-ver­ify the captcha again, then please click on the but­ton be­low to get in touch with the sup­port team.

Finally, great bat­tery life in a Framework Laptop

20 hours

Netflix 4K stream­ing250nit bright­ness, 30% vol­ume, Windows 11

17 hours

Active web us­age

250nit bright­ness, 30% vol­ume, Windows 11

11 hours

Video con­fer­enc­ing250nit bright­ness, 30% vol­ume, Windows 11

7 days

Standby with­out charg­ing

Wi-Fi con­nected on Ubuntu

Intel® Core™ Ultra Series 3 proces­sors

The Framework Laptop 13 Pro runs on Intel® Core™ Ultra Series 3 proces­sors, un­lock­ing 20 hours of bat­tery ϟ life, up to 64GB of LPCAMM2 LPDDR5X mem­ory, and sup­port for up to 8TB of PCIe Gen 5.0 NVMe stor­age. It’s de­signed to stay re­spon­sive un­der sus­tained, heavy work­loads.

Power-efficient mem­ory, made up­grade­able

We’re among the first to pair Intel® Core™ Ultra Series 3 with LPCAMM2. A high-den­sity in­ter­poser en­ables LPDDR5X in a mod­u­lar form, de­liv­er­ing 7467 MT/s and high per­for­mance per watt with­out sol­der­ing it down.

A lap­top that you own

You can cus­tomize it,

Pick your ports with the Framework Expansion Card sys­tem and in­stall them di­rectly into your lap­top with­out re­ly­ing on ex­ter­nal adapters. The mag­net-at­tach Bezel lets you cus­tomize with bold or translu­cent color op­tions.

USB-C

USB-A

Audio Jack

DisplayPort

HDMI

MicroSD

SD

Storage - 250GB

Storage - 1TB

Ethernet

re­pair it,

A truly easy-to-re­pair lap­top that’s built to re­spect your rights. Just scan the QR codes, fol­low the guides, and re­place any part with a sin­gle tool that’s in­cluded in the box.

up­grade it.

When you’re ready for more per­for­mance, you can up­grade in­di­vid­ual com­po­nents in­stead of re­plac­ing your en­tire lap­top. Install a new Mainboard for gen­er­a­tional proces­sor up­grades, add mem­ory to han­dle heav­ier work­loads, or ex­pand your stor­age to in­crease ca­pac­ity or en­able dual boot­ing. The Framework Marketplace makes it easy to find the com­pat­i­ble parts you need.

Runs Linux. Really well.

(you can also use Windows 11 if you want)

We don’t just sup­port Linux; we live in it. Framework Laptop 13 Pro with Intel® Core™ Ultra Series 3 is our first Ubuntu Certified sys­tem. We seed de­vel­op­ment hard­ware and pro­vide fund­ing to a range of other dis­tros like Fedora, Bazzite, NixOS, CachyOS, and more to en­sure re­li­able sup­port.

A sen­sory up­grade

13.5″ 2880x1920 Touchscreen Display

A cus­tom 13.5″ 3:2 touch­screen dis­play with sharp 2880×1920 res­o­lu­tion gives you the ver­ti­cal space you need for cod­ing and pro­duc­tiv­ity. A 30 – 120Hz vari­able re­fresh rate keeps mo­tion smooth while op­ti­miz­ing power, and with up to 700nits of bright­ness and a matte sur­face, it stays clear across a wide range of light­ing con­di­tions.

A hap­tic touch­pad that beats your ex­pec­ta­tions

The large 123.7mm × 76.7mm Haptic Touchpad, pow­ered by four piezo­elec­tric ac­tu­a­tors, de­liv­ers con­sis­tent, high-qual­ity clicks across the sur­face. Feedback and ges­tures are fully tun­able, so you can set it up ex­actly how you want.

The key­board you love, now even bet­ter

With 1.5mm of key travel, the key­board de­liv­ers deeper, more tac­tile feed­back than most mod­ern lap­tops with­out in­creas­ing noise. A CNC alu­minum Input Cover Frame re­duces deck flex for a more solid and con­sis­tent feel. Available in mul­ti­ple ANSI and ISO lay­outs, in black, black with laven­der, and black with gray and or­ange.

Dolby Atmos® au­dio

The side-fir­ing speak­ers are tuned with Dolby Atmos® to de­liver clear, bal­anced au­dio on Windows, ideal for calls or mu­sic while you work.

Thin, light, and fully alu­minum

At just 15.85mm thick and 1.4kg, gain­ing dura­bil­ity does­n’t mean los­ing porta­bil­ity. The Top Cover, Input Cover, and Bottom Cover are now CNC ma­chined from 6063 alu­minum, in­creas­ing rigid­ity and dura­bil­ity.

296.63mm

Width

228.98mm

Depth

15.85mm

Height

1.4kg

Weight

Open source ecosys­tems

We’ve open sourced de­sign files and doc­u­men­ta­tion for many core com­po­nents and firmware on GitHub, giv­ing you the free­dom to mod­ify, ex­tend, or re­pur­pose them.

Respecting your pri­vacy

Privacy switches

Your pri­vacy is pro­tected at a hard­ware level, with phys­i­cal switches that elec­tri­cally cut off the we­b­cam and mi­cro­phones when­ever you need.

No crap­ware

We hate soft­ware bloat as much as you do. Our pre-builts ship with Ubuntu or stock Windows 11 plus the nec­es­sary dri­vers, and our DIY Edition lets you bring whichever op­er­at­ing sys­tem you’d like.

The choice is yours

Framework Laptop 13 Pro is avail­able pre-built with Windows or Ubuntu pre-in­stalled, or as a DIY Edition that lets you in­stall the op­er­at­ing sys­tem of your choice.

Upgrade, cus­tomize, and re­pair

Pick up new parts and mod­ules for your Framework Laptop 13 Pro.

Keep track of what we’re work­ing on with the Framework Newsletter.

ϟ

Testing con­ducted by Framework in April 2026 us­ing Framework Laptop 13 Pro tested with Intel® Core™ Ultra X7 358H Processor, Intel® Arc™ B390 graph­ics, 2.8K touch­screen dis­play, 32GB mem­ory and 1TB stor­age, with dis­play bright­ness set to 250nits, dis­play re­fresh rate set to 60Hz, speaker vol­ume as 30%, Dolby Atmos® dis­abled, and wire­less en­abled. Battery life tested by stream­ing Netflix 4K con­tent in the Netflix app on Windows 11 un­der Best Power Efficiency mode. Battery life varies by use and con­fig­u­ra­tion.

A col­lec­tion of prin­ci­ples and pat­terns that shape soft­ware sys­tems, teams, and de­ci­sions.

56 laws

•

Click any card to learn more

In 2023, Raytheon’s pres­i­dent stood at the Paris Air Show and de­scribed what it took to restart Stinger mis­sile pro­duc­tion. They brought back en­gi­neers in their 70s to teach younger work­ers how to build a mis­sile from pa­per schemat­ics drawn dur­ing the Carter ad­min­is­tra­tion. Test equip­ment had been sit­ting in ware­houses for years. The nose cone still had to be at­tached by hand, ex­actly as it was forty years ago.

The Pentagon had­n’t bought a new Stinger in twenty years. Then Russia in­vaded Ukraine, and sud­denly every­one needed them. The pro­duc­tion line was shut down. The elec­tron­ics were ob­so­lete. The seeker com­po­nent was out of pro­duc­tion. An or­der placed in May 2022 would­n’t de­liver un­til 2026. Four years. Not be­cause of money. Because the peo­ple who knew how to build them re­tired a decade ear­lier and no­body re­placed them.

I run en­gi­neer­ing teams in Ukraine. My peo­ple lived the other side of this equa­tion. Not the fac­tory floor. The re­ceiv­ing end. While Raytheon was strug­gling to restart pro­duc­tion from forty-year-old blue­prints, the US was ship­ping thou­sands of Stingers to Ukraine. RTX CEO Greg Hayes: ten months of war burned through thir­teen years’ worth of Stinger pro­duc­tion. I’ve seen this pat­tern be­fore. It’s hap­pen­ing in my in­dus­try right now.

In March 2023, the EU promised Ukraine one mil­lion ar­tillery shells within twelve months. European pro­duc­tion ca­pac­ity sat at 230,000 shells per year. Ukraine was con­sum­ing 5,000 to 7,000 rounds per day. Anyone with a cal­cu­la­tor could see this would­n’t work.

By the dead­line, Europe de­liv­ered about half. Macron called the orig­i­nal promise reck­less. An in­ves­ti­ga­tion by eleven me­dia out­lets across nine coun­tries found ac­tual pro­duc­tion ca­pac­ity was roughly one-third of of­fi­cial EU claims. The mil­lion-shell mark was­n’t hit un­til December 2024, nine months late.

It was­n’t one bot­tle­neck. It was all of them. France had halted do­mes­tic pro­pel­lant pro­duc­tion in 2007. Seventeen years of noth­ing. Europe’s sin­gle ma­jor TNT pro­ducer was in Poland. Germany had two days of am­mu­ni­tion stored. A Nammo plant in Denmark was shut down in 2020 and had to be restarted from scratch. The en­tire con­ti­nen­t’s de­fense in­dus­try had been op­ti­mized for mak­ing small batches of ex­pen­sive cus­tom prod­ucts. Nobody planned for vol­ume. Nobody planned for cri­sis.

The U.S. was­n’t much bet­ter. One plant in Scranton, one fa­cil­ity in Iowa for ex­plo­sive fill, no do­mes­tic TNT pro­duc­tion since 1986. Billions of in­vest­ment later, pro­duc­tion still had­n’t hit half the tar­get.

This was­n’t an ac­ci­dent. In 1993, the Pentagon told de­fense CEOs to con­sol­i­date or die. Fifty-one ma­jor de­fense con­trac­tors col­lapsed into five. Tactical mis­sile sup­pli­ers went from thir­teen to three. Shipbuilders from eight to two. The work­force fell from 3.2 mil­lion to 1.1 mil­lion. A 65% cut.

The am­mu­ni­tion sup­ply chain had sin­gle points of fail­ure every­where. One man­u­fac­turer for 155mm shell cas­ings, sit­ting in Coachella, California, on the San Andreas Fault. One fa­cil­ity in Canada for pro­pel­lant charges. Optimized for min­i­mum cost with zero mar­gin for surge. On pa­per, ef­fi­cient. In prac­tice, one bad day away from col­lapse.

Then there’s Fogbank. A clas­si­fied ma­te­r­ial used in nu­clear war­heads. Produced from 1975 to 1989, then the fa­cil­ity was shut down. When the gov­ern­ment needed to re­pro­duce it for a war­head life ex­ten­sion pro­gram in 2000, they dis­cov­ered they could­n’t. A GAO re­port found that al­most all staff with pro­duc­tion ex­per­tise had re­tired, died, or left the agency. Few records ex­isted.

After spend­ing an ad­di­tional $69 mil­lion and years of re­verse en­gi­neer­ing, they fi­nally pro­duced vi­able Fogbank. Then dis­cov­ered the new batch was too pure. The orig­i­nal had con­tained an un­in­ten­tional im­pu­rity that was crit­i­cal to its func­tion. That fact ex­isted nowhere in any doc­u­ment. Only the work­ers who made the orig­i­nal batch knew it, and they had re­tired years ear­lier.

A nu­clear weapons pro­gram lost the abil­ity to make a ma­te­r­ial it in­vented. The knowl­edge ex­isted only in peo­ple, and the peo­ple were gone.

I read the Fogbank story and rec­og­nized it im­me­di­ately. Not the nu­clear ma­te­r­ial. The pat­tern. Build ca­pa­bil­ity over decades. Find a cheaper sub­sti­tute. Let the hu­man pipeline at­ro­phy. Enjoy the sav­ings. Then watch it all col­lapse when a cri­sis de­mands what you op­ti­mized away.

In de­fense, the sub­sti­tute was the peace div­i­dend. In soft­ware, it’s AI.

I wrote about the tal­ent pipeline col­lapse be­fore. The hir­ing num­bers and the ju­nior-to-se­nior prob­lem are doc­u­mented. So is the com­pre­hen­sion cri­sis. What I did­n’t have was the right his­tor­i­cal par­al­lel. Now I do.

And it tells you some­thing the hir­ing data does­n’t: how long re­build­ing ac­tu­ally takes.

Every ma­jor de­fense pro­duc­tion ramp-up took three to five years for sim­ple sys­tems. Five to ten for com­plex ones. Stinger: thirty months min­i­mum from or­der to de­liv­ery. Javelin: four and a half years to less than dou­ble pro­duc­tion. 155mm shells: four years and still not at tar­get de­spite five bil­lion dol­lars in­vested. France only restarted pro­pel­lant pro­duc­tion in 2024, sev­en­teen years af­ter shut­ting it down.

Money was never the con­straint. Knowledge was. RAND found that 10% of tech­ni­cal skills for sub­ma­rine de­sign need ten years of on-the-job ex­pe­ri­ence to de­velop, some­times fol­low­ing a PhD. Apprenticeships in de­fense trades take two to four years, with five to eight years to reach su­per­vi­sory com­pe­tence.

Now map that onto soft­ware. A ju­nior de­vel­oper needs three to five years to be­come a com­pe­tent mid-level en­gi­neer. Five to eight years to be­come se­nior. Ten or more to be­come a prin­ci­pal or ar­chi­tect. That time­line can’t be com­pressed by throw­ing money at it. It can’t be com­pressed by AI ei­ther.

A METR ran­dom­ized con­trolled trial found that ex­pe­ri­enced de­vel­op­ers us­ing AI cod­ing tools ac­tu­ally took 19% longer on real-world open source tasks. Before start­ing, they pre­dicted AI would make them 24% faster. The gap be­tween pre­dic­tion and re­al­ity was 43 per­cent­age points. When re­searchers tried to run a fol­low-up, a sig­nif­i­cant share of de­vel­op­ers re­fused to par­tic­i­pate if it meant work­ing with­out AI. They could­n’t imag­ine go­ing back.

The soft­ware in­dus­try is in year three of the same op­ti­miza­tion. Salesforce said it won’t hire more soft­ware en­gi­neers in 2025. A LeadDev sur­vey found 54% of en­gi­neer­ing lead­ers be­lieve AI copi­lots will re­duce ju­nior hir­ing long-term. A CRA sur­vey of uni­ver­sity com­put­ing de­part­ments found 62% re­ported de­clin­ing en­roll­ment this year.

I see it in code re­view. Review is now the bot­tle­neck. AI gen­er­ates code fast. Humans re­view it slow. The in­dus­try’s an­swer is pre­dictable: let AI re­view AI’s code. I’m not do­ing that. I’ve re­worked our pull re­quest tem­plates in­stead. Every PR now has to ex­plain what changed, why, what type of change it is, screen­shots of be­fore and af­ter. Structured con­text so the re­viewer is­n’t guess­ing. I’m adding ded­i­cated re­view­ers per pro­ject. More eyes, more chances to catch what the model missed.

But even that does­n’t solve the deeper prob­lem. The skills you need to be ef­fec­tive now are dif­fer­ent. Technical ex­per­tise alone is­n’t enough any­more. You need peo­ple who can take own­er­ship, com­mu­ni­cate trade­offs, push back on bad sug­ges­tions from a ma­chine that sounds very con­fi­dent. Leadership qual­i­ties. Our last hir­ing round tells you how rare that is: 2,253 can­di­dates, 2,069 dis­qual­i­fied, 4 hired. A 0.18% con­ver­sion rate. The com­bi­na­tion of tech­ni­cal skill and the judg­ment to know when the AI is wrong barely ex­ists in the mar­ket any­more.

We doc­u­ment every­thing. Site Books, SDDs, RVS re­ports, boil­er­plate mod­ules with full cov­er­age. It works to­day, be­cause the peo­ple read­ing those docs have the en­gi­neer­ing ex­per­tise to act on them. What hap­pens when they don’t? Honestly, I don’t know. Maybe AI in five years is good enough that it won’t mat­ter. Maybe the prob­lem stays man­age­able. I can’t pre­dict the ca­pa­bil­i­ties of mod­els in 2031.

But crises don’t send cal­en­dar in­vites. Nobody ex­pected a full-scale land war in Europe in 2022. The de­fense in­dus­try had thirty years to pre­pare and did­n’t. Even Fogbank had records. They weren’t enough with­out the peo­ple who un­der­stood what they meant.

Five to ten years from now, we’ll need se­nior en­gi­neers. People who un­der­stand sys­tems end to end, who can de­bug dis­trib­uted fail­ures at 2 AM, who carry in­sti­tu­tional knowl­edge that ex­ists nowhere in the code­base. Those en­gi­neers don’t ex­ist yet be­cause we’re not cre­at­ing them. The ju­niors who should be learn­ing right now are ei­ther not be­ing hired or de­vel­op­ing what a DoD-funded work­force study calls “AI-mediated com­pe­tence.” They can prompt an AI. They can’t tell you what the AI got wrong.

It’s Fogbank for code. When ju­niors skip de­bug­ging and skip the for­ma­tive mis­takes, they don’t build the tacit ex­per­tise. And when my gen­er­a­tion of en­gi­neers re­tires, that knowl­edge does­n’t trans­fer to the AI.

It just dis­ap­pears.

The West al­ready made this mis­take once. The bill came due in Ukraine.

I know how this sounds. I know I’ve writ­ten about the tal­ent pipeline be­fore. The de­fense ex­am­ple is­n’t about re­peat­ing the ar­gu­ment. It’s about show­ing what hap­pens if the in­dus­try’s ex­pec­ta­tions don’t work out. Stinger, Javelin, Fogbank, a mil­lion shells no­body could make. That’s the cost of bet­ting wrong on op­ti­miza­tion. We’re mak­ing the same bet with soft­ware en­gi­neer­ing right now.

Maybe AI gets good enough, and the bet pays off. Maybe it does­n’t. The de­fense in­dus­try thought peace would last for­ever, too.

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Organizations de­sign sys­tems that mir­ror their own com­mu­ni­ca­tion struc­ture.

Premature op­ti­miza­tion is the root of all evil.

With a suf­fi­cient num­ber of API users, all ob­serv­able be­hav­iors of your sys­tem will be de­pended on by some­body.

Leave the code bet­ter than you found it.

YAGNI (You Aren’t Gonna Need It)

Don’t add func­tion­al­ity un­til it is nec­es­sary.

Adding man­power to a late soft­ware pro­ject makes it later.

A com­plex sys­tem that works is in­vari­ably found to have evolved from a sim­ple sys­tem that worked.

All non-triv­ial ab­strac­tions, to some de­gree, are leaky.

Every ap­pli­ca­tion has an in­her­ent amount of ir­re­ducible com­plex­ity that can only be shifted, not elim­i­nated.

A dis­trib­uted sys­tem can guar­an­tee only two of: con­sis­tency, avail­abil­ity, and par­ti­tion tol­er­ance.

Small, suc­cess­ful sys­tems tend to be fol­lowed by ov­erengi­neered, bloated re­place­ments.

A set of eight false as­sump­tions that new dis­trib­uted sys­tem de­sign­ers of­ten make.

Every pro­gram at­tempts to ex­pand un­til it can read mail.

There is a cog­ni­tive limit of about 150 sta­ble re­la­tion­ships one per­son can main­tain.

The square root of the to­tal num­ber of par­tic­i­pants does 50% of the work.

Those who un­der­stand tech­nol­ogy don’t man­age it, and those who man­age it don’t un­der­stand it.

In a hi­er­ar­chy, every em­ployee tends to rise to their level of in­com­pe­tence.

The min­i­mum num­ber of team mem­bers whose loss would put the pro­ject in se­ri­ous trou­ble.

Companies tend to pro­mote in­com­pe­tent em­ploy­ees to man­age­ment to limit the dam­age they can do.

Work ex­pands to fill the time avail­able for its com­ple­tion.

The first 90% of the code ac­counts for the first 90% of de­vel­op­ment time; the re­main­ing 10% ac­counts for the other 90%.

It al­ways takes longer than you ex­pect, even when you take into ac­count Hofstadter’s Law.

When a mea­sure be­comes a tar­get, it ceases to be a good mea­sure.

Anything you need to quan­tify can be mea­sured in some way bet­ter than not mea­sur­ing it.

Anything that can go wrong will go wrong.

Be con­ser­v­a­tive in what you do, be lib­eral in what you ac­cept from oth­ers.

Technical Debt is every­thing that slows us down when de­vel­op­ing soft­ware.

Given enough eye­balls, all bugs are shal­low.

Debugging is twice as hard as writ­ing the code in the first place.

A pro­ject should have many fast unit tests, fewer in­te­gra­tion tests, and only a small num­ber of UI tests.

Repeatedly run­ning the same tests be­comes less ef­fec­tive over time.

Software that re­flects the real world must evolve, and that evo­lu­tion has pre­dictable lim­its.

90% of every­thing is crap.

The speedup from par­al­leliza­tion is lim­ited by the frac­tion of work that can­not be par­al­lelized.

It is pos­si­ble to achieve sig­nif­i­cant speedup in par­al­lel pro­cess­ing by in­creas­ing the prob­lem size.

The value of a net­work is pro­por­tional to the square of the num­ber of users.

Every piece of knowl­edge must have a sin­gle, un­am­bigu­ous, au­thor­i­ta­tive rep­re­sen­ta­tion.

Designs and sys­tems should be as sim­ple as pos­si­ble.

Five main guide­lines that en­hance soft­ware de­sign, mak­ing code more main­tain­able and scal­able.

An ob­ject should only in­ter­act with its im­me­di­ate friends, not strangers.

Software and in­ter­faces should be­have in a way that least sur­prises users and other de­vel­op­ers.

The less you know about some­thing, the more con­fi­dent you tend to be.

Never at­tribute to mal­ice that which is ad­e­quately ex­plained by stu­pid­ity or care­less­ness.

The sim­plest ex­pla­na­tion is of­ten the most ac­cu­rate one.

Sticking with a choice be­cause you’ve in­vested time or en­ergy in it, even when walk­ing away helps you.

The Map Is Not the Territory

Our rep­re­sen­ta­tions of re­al­ity are not the same as re­al­ity it­self.

A ten­dency to fa­vor in­for­ma­tion that sup­ports our ex­ist­ing be­liefs or ideas.

We tend to over­es­ti­mate the ef­fect of a tech­nol­ogy in the short run and un­der­es­ti­mate the im­pact in the long run.

The longer some­thing has been in use, the more likely it is to con­tinue be­ing used.

Breaking a com­plex prob­lem into its most ba­sic blocks and then build­ing up from there.

Solving a prob­lem by con­sid­er­ing the op­po­site out­come and work­ing back­ward from it.

80% of the prob­lems re­sult from 20% of the causes.

The best way to get the cor­rect an­swer on the Internet is not to ask a ques­tion, it’s to post the wrong an­swer.