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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, 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 $69 mil­lion in cost over­runs and years of failed at­tempts, they fi­nally pro­duced vi­able Fogbank. Then dis­cov­ered the new batch was too pure. The orig­i­nal process had re­lied on an un­in­ten­tional im­pu­rity that was crit­i­cal to the ma­te­ri­al’s func­tion. Nobody knew. Not the en­gi­neers try­ing to re­pro­duce it. Not even the orig­i­nal work­ers who made it decades ear­lier. Los Alamos called it an un­know­ing de­pen­dency in the orig­i­nal process.

A nu­clear weapons pro­gram lost the abil­ity to make a ma­te­r­ial it in­vented. The knowl­edge did­n’t just leave with peo­ple. It was never fully un­der­stood by any­one.

(Correction: the orig­i­nal ver­sion stated that the work­ers who made Fogbank knew about the im­pu­rity. They did­n’t. The de­pen­dency was un­wit­ting, which makes the knowl­edge-loss ar­gu­ment stronger, not weaker. Thanks to John F. in the com­ments for catch­ing this.)

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. There weren’t enough. The orig­i­nal work­ers did­n’t fully un­der­stand their own process.

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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I am build­ing a cloud

2026 – 04-22

Today is fundrais­ing an­nounce­ment day. As is the na­ture of writ­ing for a larger au­di­ence, it is a for­mal, safe an­nounce­ment. As it should be. Writing must nec­es­sar­ily be­come im­per­sonal at scale. But I would like to write some­thing per­sonal about why I am do­ing this. What is the goal of build­ing exe.dev? I am al­ready the co-founder of one startup that is do­ing very well, sell­ing a prod­uct I love as much as when I first helped de­sign and build it.

What could pos­sess me to go through all the pain of start­ing an­other com­pany? Some fel­low founders have looked at me with in­credulity and shock that I would throw my­self back into the fry­ing pan. (Worse yet, ex­pe­ri­ence tells me that most of the pain is still in my fu­ture.) It has been a gen­uinely hard ques­tion to an­swer be­cause I start search­ing for a “big” rea­son, a prin­ci­ple or a so­cial need, a rea­son or mo­ti­va­tion be­yond chal­lenge. But I be­lieve the truth is far sim­pler, and to some I am sure al­most equally in­cred­u­lous.

I like com­put­ers.

In some tech cir­cles, that is an un­usual state­ment. (“In this house, we curse com­put­ers!”) I get it, com­put­ers can be re­ally frus­trat­ing. But I like com­put­ers. I al­ways have. It is re­ally fun get­ting com­put­ers to do things. Painful, sure, but the re­sults are worth it. Small mi­cro­con­trollers are fun, desk­tops are fun, phones are fun, and servers are fun, whether racked in your base­ment or in a data cen­ter across the world. I like them all.

So it is no small thing for me when I ad­mit: I do not like the cloud to­day.

I want to. Computers are great, whether it is a BSD in­stalled di­rectly on a PC or a Linux VM. I can en­joy Windows, BeOS, Novell NetWare, I even in­stalled OS/2 Warp back in the day and had a great time with it. Linux is par­tic­u­larly pow­er­ful to­day and a source of end­less po­ten­tial. And for all the pages of prod­ucts, the cloud is just Linux VMs. Better, they are API dri­ven Linux VMs. I should be in heaven.

But every cloud prod­uct I try is wrong. Some are bet­ter than oth­ers, but I am con­stantly con­strained by the choices cloud ven­dors make in ways that make it hard to get com­put­ers to do the things I want them to do.

These is­sues go be­yond UX or bad API de­sign. Some of the fun­da­men­tal build­ing blocks of to­day’s clouds are the wrong shape. VMs are the wrong shape be­cause they are tied to CPU/memory re­sources. I want to buy some CPUs, mem­ory, and disk, and then run VMs on it. A Linux VM is a process run­ning in an­other Linux’s cgroup, I should be able to run as many as I like on the com­puter I have. The only way to do that eas­ily on to­day’s clouds is to take iso­la­tion into my own hands, with gVi­sor or nested vir­tu­al­iza­tion on a sin­gle cloud VM, pay­ing the nest­ing per­for­mance penalty, and then I am left with the job of run­ning and man­ag­ing, at a min­i­mum, a re­verse proxy onto my VMs. All be­cause the cloud ab­strac­tion is the wrong shape.

Clouds have tried to solve this with “PaaS” sys­tems. Abstractions that are in­her­ently less pow­er­ful than a com­puter, be­spoke to a par­tic­u­lar provider. Learn a new way to write soft­ware for each com­pute ven­dor, only to find half way into your pro­ject that some­thing that is easy on a nor­mal com­puter is nearly im­pos­si­ble be­cause of some ob­scure limit of the plat­form sys­tem buried so deep you can­not find it un­til you are deeply com­mit­ted to a pro­ject. Time and again I have said “this is the one” only to be be­trayed by some half-assed, half-im­ple­mented, or half-thought-through ab­strac­tion. No thank you.

Consider disk. Cloud providers want you to use re­mote block de­vices (or some­thing even more lim­ited and slow, like S3). When re­mote block de­vices were in­tro­duced they made sense, be­cause com­put­ers used hard dri­ves. Remote does not hurt se­quen­tial read/​write per­for­mance, if the buffer­ing im­ple­men­ta­tion is good. Random seeks on a hard drive take 10ms, so 1ms RTT for the Ethernet con­nec­tion to re­mote stor­age is a fine price to pay. It is a good prod­uct for hard dri­ves and makes the cloud ven­dor’s life a lot eas­ier be­cause it re­moves an en­tire di­men­sion from their stan­dard in­stance types.

But then we all switched to SSD. Seek time went from 10 mil­lisec­onds to 20 mi­crosec­onds. Heroic ef­forts have cut the net­work RTT a bit for re­ally good re­mote block sys­tems, but the IOPS over­head of re­mote sys­tems went from 10% with hard dri­ves to more than 10x with SSDs.

It is a lot of work to con­fig­ure an EC2 VM to have 200k IOPS, and you will pay $10k/month for the priv­i­lege. My MacBook has 500k IOPS. Why are we hob­bling our cloud in­fra­struc­ture with slow disk?

Finally net­work­ing. Hyperscalers have great net­works. They charge you the earth for them and make it mis­er­able to do deals with other ven­dors. The stan­dard price for a GB of egress from a cloud provider is 10x what you pay rack­ing a server in a nor­mal data cen­ter. At mod­er­ate vol­ume the mul­ti­plier is even worse. Sure, if you spend $XXm/month with a cloud the prices get much bet­ter, but most of my pro­jects want to spend $XX/month, with­out the lit­tle m. The fun­da­men­tal tech­nol­ogy here is fine, but this is where lim­its are placed on you to make sure what­ever you build can­not be af­ford­able.

Finally, clouds have painful APIs. This is where pro­jects like K8S come in, pa­per­ing over the pain so en­gi­neers suf­fer a bit less from us­ing the cloud. But VMs are hard with Kubernetes be­cause the cloud makes you do it all your­self with lumpy nested vir­tu­al­iza­tion. Disk is hard be­cause back when they were de­sign­ing K8S Google did­n’t re­ally even do us­able re­mote block de­vices, and even if you can find a com­mon pat­tern among clouds to­day to pa­per over, it will be slow. Networking is hard be­cause if it were easy you would pri­vate link in a few sys­tems from a neigh­bor­ing open DC and drop a zero from your cloud spend. It is tempt­ing to dis­miss Kubernetes as a scam, ar­ti­fi­cial make work de­signed to avoid do­ing real prod­uct work, but the truth is worse: it is a prod­uct at­tempt­ing to solve an im­pos­si­ble prob­lem: make clouds portable and us­able. It can­not be done.

You can­not solve the fun­da­men­tal prob­lems with cloud ab­strac­tions by build­ing new ab­strac­tions on top. Making Kubernetes good is in­her­ently im­pos­si­ble, a pro­ject in putting (admittedly high qual­ity) lip­stick on a pig.

We have been mud­dy­ing along with these mis­er­able clouds for 15 years now. We make do, in the way we do with all the un­pleas­ant parts of our soft­ware stack, hold­ing our nose when­ever we have to deal with and try­ing to min­i­mize how of­ten that hap­pens.

This how­ever, is the mo­ment to fix it.

This is the mo­ment be­cause some­thing has changed: we have agents now. (Indeed my co-founder Josh and I started tin­ker­ing be­cause we wanted to use LLMs in pro­gram­ming. It turns out what needs build­ing for LLMs are bet­ter tra­di­tional ab­strac­tions.) Agents, by mak­ing it eas­i­est to write code, means there will be a lot more soft­ware. Economists would call this an in­stance of Jevons para­dox. Each of us will write more pro­grams, for fun and for work. We need pri­vate places to run them, easy shar­ing with friends and col­leagues, min­i­mal over­head.

With more to­tal soft­ware in our lives the cloud, which was an an­noy­ing pain, be­comes a much big­ger pain. We need a lot more com­pute, we need it to be eas­ier to man­age. Agents help to some de­gree. If you trust them with your cre­den­tials they will do a great job dri­ving the AWS API for you (though oc­ca­sion­ally it will delete your pro­duc­tion DB). But agents strug­gle with the fun­da­men­tal lim­its of the ab­strac­tions as much as we do. You need more to­kens than you should and you get a worse re­sult than you should. Every per­cent of con­text win­dow the agent spends think­ing about how to con­tort clas­sic clouds into work­ing is con­text win­dow is not us­ing to solve your prob­lem.

So we are go­ing to fix it. What we have launched on exe.dev to­day ad­dresses the VM re­source iso­la­tion prob­lem: in­stead of pro­vi­sion­ing in­di­vid­ual VMs, you get CPU and mem­ory and run the VMs you want. We took care of a TLS proxy and an au­then­ti­ca­tion proxy, be­cause I do not ac­tu­ally want my fresh VMs dumped di­rectly on the in­ter­net. Your disk is lo­cal NVMe with blocks repli­cated off ma­chine asyn­chro­nously. We have re­gions around the world for your ma­chines, be­cause you want your ma­chines close. Your ma­chines are be­hind an any­cast net­work to give all your global users a low la­tency en­try­point to your prod­uct (and so we can build some new ex­cit­ing things soon).

There is a lot more to build here, from ob­vi­ous things like sta­tic IPs to UX chal­lenges like how to give you ac­cess to our au­to­matic his­tor­i­cal disk snap­shots. Those will get built. And at the same time we are go­ing right back to the be­gin­ning, rack­ing com­put­ers in data cen­ters, think­ing through every layer of the soft­ware stack, ex­plor­ing all the op­tions for how we wire up net­works.

So, I am build­ing a cloud. One I ac­tu­ally want to use. I hope it is use­ful to you.