👋 Welcome to Climate Drift: your cheat-sheet to cli­mate. Each edi­tion breaks down real so­lu­tions, hard num­bers, and ca­reer moves for op­er­a­tors, founders, and in­vestors who want im­pact. For more: Community | Accelerator | Open Climate Firesides | Deep Dives

You know that feel­ing when you’re wait­ing for the ca­ble guy, and they said ’between 8am and 6pm, and you waste your en­tire day, and they never show up?

Now imag­ine that, ex­cept the ca­ble guy is ‘electricity,’ the day is ‘50 years,’ and you’re one of 600 mil­lion peo­ple. At some point, you stop wait­ing and fig­ure it out your­self.

What’s hap­pen­ing across Sub-Saharan Africa right now is the most am­bi­tious in­fra­struc­ture pro­ject in hu­man his­tory, ex­cept it’s not be­ing built by gov­ern­ments or util­i­ties or World Bank con­sor­tiums. It’s be­ing built by star­tups sell­ing so­lar pan­els to farm­ers on pay­ment plans. And it’s work­ing.

Over 30 mil­lion so­lar prod­ucts sold in 2024. 400,000 new so­lar in­stal­la­tions every month across Africa. 50% mar­ket share cap­tured by com­pa­nies that did­n’t ex­ist 15 years ago. Carbon cred­its sub­si­diz­ing the cost. IoT chips in every de­vice. 90%+ re­pay­ment rates on loans to peo­ple earn­ing $2/day.

And if you un­der­stand what’s hap­pen­ing in Africa, you un­der­stand the tem­plate for how in­fra­struc­ture will get built every­where else for the next 50 years.

Today we are look­ing into:

* Why the grid will never come (and why that’s ac­tu­ally good news)

* How it takes three con­verg­ing mir­a­cles (cheap hard­ware, zero-cost pay­ments, and pay-as-you-go)

* 2 case stud­ies on how it works on the ground

* Whether this tem­plate works be­yond Africa (spoiler: it al­ready is)

The next co­hort of our ac­cel­er­a­tor launches soon, and ap­pli­ca­tions are still open (but spots are lim­ited). If you’re ready to fight cli­mate change, don’t wait:

Here’s a stat that should make you an­gry: 600 mil­lion peo­ple in Sub-Saharan Africa lack re­li­able elec­tric­ity. Not be­cause the tech­nol­ogy does­n’t ex­ist. Not be­cause they don’t want it. But be­cause the unit eco­nom­ics of grid ex­ten­sion to rural ar­eas are com­pletely, ut­terly, ir­re­deemably fucked.

The tra­di­tional de­vel­op­ment play­book goes some­thing like this: Chapter 1, build cen­tral­ized power gen­er­a­tion. Chapter 2, string trans­mis­sion lines across hun­dreds of kilo­me­ters. Chapter 3, dis­trib­ute to mil­lions of homes. Chapter 4, col­lect pay­ments. Chapter 5, main­tain the whole thing for­ever.

This worked great if you were elec­tri­fy­ing America in the 1930s, when la­bor was cheap, ma­te­ri­als were sub­si­dized, and the gov­ern­ment could strong-arm right-of-way ac­cess. It works less great when you’re try­ing to reach a farmer four hours from the near­est paved road who earns $600 per year.

Let me show you the math:

* Cost to con­nect one rural house­hold to the grid: $266 to $2,000

* Payback pe­riod: 13-200 months (if you can even col­lect pay­ments)

So util­i­ties do what any ra­tio­nal ac­tor would do: they stop build­ing where the math stops work­ing. Which is ex­actly where the peo­ple are.

This has been the de­vel­op­ment sec­tor’s dirty lit­tle se­cret for 50 years. “We’re work­ing on grid ex­ten­sion!” Translation: we’re not work­ing on grid ex­ten­sion be­cause the eco­nom­ics are im­pos­si­ble, but we need to say we’re work­ing on it so we keep get­ting donor money.

Meanwhile, 1.5 bil­lion peo­ple spend up to 10% of their in­come on kerosene, diesel, and other dirty fu­els. They walk hours to charge their phones. They can’t re­frig­er­ate med­i­cine or food. Their kids can’t study af­ter dark. Women in­hale cook­ing smoke equiv­a­lent to two packs of cig­a­rettes daily.

While every­one was ar­gu­ing about feed-in tar­iffs and util­ity-scale so­lar, some­thing wild hap­pened to so­lar costs:

That’s a 99.5% de­cline in 45 years. Moore’s Law ex­cept for sun­shine.

Want to learn how so­lar got cheap? Welcome to Climate Drift - the place where we dive into cli­mate so­lu­tions and help you find your role in the race to net zero…

But here’s what’s even cra­zier: the price of com­plete so­lar home sys­tems:

Battery costs also col­lapsed 90%. Inverters got cheap. LED bulbs got ef­fi­cient. Manufacturing in China got in­sanely good. Logistics in Africa got in­sanely bet­ter.

All of these trends con­verged around 2018-2020, and sud­denly the eco­nom­ics of off-grid so­lar just… flipped. The hard­ware be­came a solved prob­lem.

But there was still a mas­sive, seem­ingly in­sur­mount­able bar­rier: $120 up­front might as well be $1 mil­lion when you earn $2/day.

This is where the story gets in­ter­est­ing.

Quick his­tory les­son: In 2007, Safaricom (Kenya’s telco) launched M-PESA, a mo­bile money plat­form that let peo­ple trans­fer cash via SMS.

Everyone thought it would fail. Why would any­one use their phone to send money?

By 2025: 70% of Kenyans use mo­bile money. Not in ad­di­tion to banks. Instead of banks. Kenya processes more mo­bile money trans­ac­tions per capita than any coun­try on Earth.

It worked be­cause it solved a real prob­lem: Kenyans were al­ready send­ing money through in­for­mal net­works. M-PESA just made it cheaper and safer.

Here’s why this mat­ters: M-PESA cre­ated a pay­ment rail with near-zero trans­ac­tion costs. Which means you can eco­nom­i­cally col­lect tiny pay­ments. $0.21 per day pay­ments.

This broke open a fi­nanc­ing model that changes every­thing: Pay-As-You-Go.

This is the un­lock. This is the thing that makes every­thing else pos­si­ble.

The sys­tem has a GSM chip that calls home­After 30 months = you own it, free power for­ever

The magic is this: You’re not buy­ing a $1,200 so­lar sys­tem. You’re re­plac­ing $3-5/week kerosene spend­ing with a $0.21/day so­lar sub­scrip­tion (so with $1.5 per week half the price of kerosene) that’s cheaper AND gives you bet­ter light, phone charg­ing, ra­dio, and no res­pi­ra­tory dis­ease.

The de­fault rate? 90%+ of cus­tomers re­pay on time.

Why? Because the as­set ac­tu­ally works. It de­liv­ers value every sin­gle day. The al­ter­na­tive is go­ing back to kerosene lamps in the dark. Nobody wants that.

This is the “innovation” that every­one missed. The hard­ware got cheap, but PAYG made it ac­ces­si­ble. And mo­bile money made PAYG eco­nom­i­cally vi­able.

Now let’s talk about what hap­pens when you com­bine these three things with 2 case stud­ies.

23 mil­lion so­lar prod­ucts sold in 2023, serv­ing 40 mil­lion cus­tomers in 42 coun­tries, and tar­get­ing 50 mil­lion units by 2026.

Their prod­uct range spans from hand­held so­lar lamps to multi-room home so­lar kits and clean LPG stoves

Want to dive deeper? I got a cas­es­tudy for youHow Pay-As-You-Go so­lar can un­lock en­ergy eq­uity in Africa👋 Welcome to Climate Drift: your cheat-sheet to cli­mate. Each edi­tion breaks down real so­lu­tions, hard num­bers, and ca­reer moves for op­er­a­tors, founders, and in­vestors who want im­pact. For more: Community | Accelerator | Open Climate Firesides | Deep Dives…

Each turn of the wheel makes the next turn eas­ier. This is a com­pound­ing moat.

And here’s what no­body out­side Africa un­der­stands: Sun King has 50%+ mar­ket share in their cat­e­gory. They’re not scrappy startup. They’re a dom­i­nant in­fra­struc­ture provider.

This would be like if one startup owned 50% of U. S. home so­lar. Except the im­pact and the TAM is big­ger be­cause there’s no in­cum­bent grid to com­pete with.

If Sun King is the light­ing/​house­hold elec­tri­fi­ca­tion play, SunCulture is the agri­cul­ture pro­duc­tiv­ity play. And the num­bers are even more in­sane.

* Farmers go from $600/acre to $14,000/acre rev­enue

* Zero mar­ginal cost af­ter pay­off (no diesel!)

Okay, this is where it gets re­ally spicy.

Remember that SunCulture so­lar pump dis­plac­ing diesel? That’s 2.9 tons of CO2 avoided per year. Per pump.

Multiply by 47,000 pumps = 136,000 tons CO2/year. Over seven years = 3+ mil­lion tons cu­mu­la­tive.

Want to dive deeper? I got an­other cas­es­tudy for you👋 Welcome to Climate Drift: your cheat-sheet to cli­mate. Each edi­tion breaks down real so­lu­tions, hard num­bers, and ca­reer moves for op­er­a­tors, founders, and in­vestors who want im­pact. For more: Community | Accelerator | Open Climate Firesides | Deep Dives…

Now here’s the hack: Someone will pay for that.

Enter car­bon cred­its. SunCulture is the first African so­lar ir­ri­ga­tion com­pany with Verra-registered car­bon cred­its. Each ton of avoided CO2 can be sold for $15-30 (high-quality agri­cul­tural cred­its, not sketchy for­est off­sets).

Let’s do the fly­wheel again, but this time tur­bocharged with car­bon cred­its.

It gets even bet­ter: there are peo­ple who will pay for cred­its be­fore­hand.

British International Investment (UK’s DFI) pi­o­neered this with SunCulture: they pro­vided $6.6M in “carbon-backed equip­ment fi­nanc­ing.” They bear the car­bon price risk, SunCulture gets up­front cap­i­tal, farm­ers get 25-40% cheaper pumps.

This is how it should be: The cli­mate im­pact that was an ex­ter­nal­ity is now a rev­enue stream. The global North’s car­bon prob­lem sub­si­dizes the global South’s en­ergy ac­cess.

A quick note on MRV

Okay, so you might know I have… is­sues with the car­bon credit world, es­pe­cially MRV(monitoring, re­port­ing, ver­i­fi­ca­tion). Here mon­i­tor­ing is IoT-based, the MRV costs are near-zero. No ex­pen­sive field au­dits. The teleme­try data proves the pump is run­ning = proves diesel is dis­placed = proves car­bon is avoided.

The car­bon credit mech­a­nism turns cli­mate in­fra­struc­ture into an as­set class. Which means you can fi­nance it at scale.

Btw this is how the largest for­est of the US is now be­ing fi­nanced:

Chestnut Carbon buys de­graded farm­land across the Southeast, re­plants bio­di­verse na­tive forests, ver­i­fies long-term car­bon re­moval, and signs long-dated off­take deals with blue-chip buy­ers like Microsoft. The com­pany has ac­quired more than 35,000 acres, planted over 17 mil­lion trees, and aims to re­store 100,000+ acres by 2030 with an ex­pected 100 mil­lion tons of CO2 re­moved over 50 years.

Learn more here: 👋 Welcome to Climate Drift: your cheat-sheet to cli­mate. Each edi­tion breaks down real so­lu­tions, hard num­bers, and ca­reer moves for op­er­a­tors, founders, and in­vestors who want im­pact. For more: Community | Accelerator | Open Climate Firesides | Deep Dives…

So: what now?

Why is the mar­ket con­cen­trated? Because the full-stack is re­ally fuck­ing hard.

Most com­pa­nies can do 2-3 of these. The win­ners do all 10.

This cre­ates mas­sive bar­ri­ers to en­try and long-term moats. New en­trants can’t just show up with cheaper pan­els. The moat is the full-stack ex­e­cu­tion.

​​Let’s do the math on how big this can get.

And that’s just Africa. Add Asia (1 bil­lion with­out elec­tric­ity) and you’re north of $300B-$500B.

But here’s the thing: this mas­sively un­der­states the op­por­tu­nity.

The so­lar sys­tem is the Trojan horse. The real busi­ness is the fi­nan­cial re­la­tion­ship with 40 mil­lion cus­tomers.

Because what you’re re­ally do­ing is cre­at­ing a dig­i­tal in­fra­struc­ture layer that en­ables:

So the ac­tual TAM? It’s what­ever the to­tal con­sumer spend­ing is for 600M peo­ple ris­ing into the mid­dle class.

Okay, let’s zoom out. What hap­pens when 100M+ peo­ple get elec­tri­fied through this model?

But here’s the meta-point: This is the tem­plate for build­ing in­fra­struc­ture in the 21st cen­tury.

Not gov­ern­ment-led. Not cen­tral­ized. Not re­quir­ing 30-year megapro­jects.

Instead: mod­u­lar, dis­trib­uted, dig­i­tally-me­tered, re­motely-mon­i­tored, PAYG-financed, car­bon-sub­si­dized in­fra­struc­ture de­ployed by pri­vate com­pa­nies in com­pet­i­tive mar­kets.

This is how things will get built go­ing for­ward.

So what could go wrong?

Let’s start by mak­ing clear this is not a one size fits all so­lu­tion:

PAYG so­lar works for house­holds and small­hold­ers. Doesn’t work for fac­to­ries or heavy in­dus­try. This is­n’t a com­plete grid re­place­ment.

1. FX Risk Companies raise dol­lars, buy hard­ware in dol­lars, col­lect rev­enue in Naira/Shillings. Currency crashes can blow up unit eco­nom­ics overnight.

2. Political/Regulatory Risk

Governments could im­pose lend­ing re­stric­tions, tar­iffs on so­lar im­ports, or sub­si­dize grid/​diesel to pro­tect state util­i­ties.

3. Default Risk

10% de­fault rate is good but frag­ile. Economic shocks, droughts, or po­lit­i­cal in­sta­bil­ity could spike de­faults.

4. Maintenance Complexity

Panels last 25 years, bat­ter­ies 5 years, pumps break. Building ser­vice net­works across rural Africa is ex­pen­sive.

5. Carbon Price Volatility

Carbon cred­its crashed from $30/ton to $5/ton in 2024. If 25-40% of af­ford­abil­ity comes from car­bon rev­enue, price swings hurt.

6. Competition from Grid

What if gov­ern­ments ac­tu­ally build the grid? (Unlikely given eco­nom­ics, but pos­si­ble with enough sub­sidy)

Port con­ges­tion, cus­toms de­lays, tar­iff swings, China ex­port con­trols, and last-mile lo­gis­tics can de­lay in­stalls, raise COGS, and tie up work­ing cap­i­tal.

Fun fact: Sun King is now pro­duc­ing their de­vices in Africa, cut­ting $300 Million in im­ports over the next years.

Okay, the bear case is im­por­tant. But let’s talk about the sce­nar­ios where this does­n’t just work: it goes 🏒.

Solar pan­els dropped 99.5% in 45 years. What if we’re only halfway through?

For as long as I have pub­lished my books, one of my over­ar­ch­ing goals was to give credit to those who ac­tu­ally in­vented the hard­ware and soft­ware that we use.

I have spent 10,000+ hours to cre­ate an ac­cu­rate record of their work but I’m not com­plain­ing. The ‘as-close-to-possible’ truth of in­ven­tion by in­di­vid­u­als or teams meant iden­ti­fy­ing the work, ed­u­cat­ing my­self, writ­ing ques­tions, and send­ing emails. And af­ter that process, I set up a chat be­cause it all gets down to talk­ing to some­one on the other side of the world, about some­thing that hap­pened 30 or 40 years ago.

If the in­ven­tion in­volves a team, I try to in­ter­view more than one per­son, so I can cross-check the facts. Not to call any­one out, it’s just that, given time, we all for­get the facts. And every­one adds their per­sonal take. It’s be­cause of that, for ex­am­ple, that I know the English mu­si­cian Peter Gabriel re­ally did visit Apple’s re­search labs as they tested the Apple Sound Chip, and gave the team his per­sonal ap­proval to use the song ‘Red Rain’ for the Macintosh II launch. Wil Oxford, Steve Perlman, Mike Potel, Mark Lentczner and Steve Milne told me so.

As I was wrap­ping up Version 2.3 of Inventing the Future, I spoke with Steve M and Mark about the AIFF (Audio Interchange File Format) au­dio stan­dard that they built around the same time as their VIP visit. They did so as pro­fes­sional pro­gram­mers, am­a­teur mu­si­cians and elec­tronic mu­sic ex­perts. Milne and Lentczner knew users needed a stan­dard file for­mat to make their work lives eas­ier and to fend off con­fu­sion in the nascent MIDI mar­ket­place. But it did­n’t ex­ist. So Steve and Mark con­sulted with users and man­u­fac­tur­ers in the Apple cafe­te­ria af­ter hours. This work is in­ter­est­ing on its own but it also un­der­pinned other re­search. The AIFF, Apple Sound Chip, and MIDI Manager work scaf­folded QuickTime and its ex­ten­si­ble video for­mats and pro­grams in 1991. Senior en­gi­neer Toby Farrand told me:

Audio drove the de­vel­op­ment of QuickTime more than any­thing.

So who or what drove the de­vel­op­ment of AIFF?

Steve and Mark re­ferred me to the IFF (Interchange File Format (IFF) and the TIFF (Tag Image File Format) that were built be­fore AIFF, in 1985 and 1986 re­spec­tively. These file for­mats were the bench­mark for open me­dia stan­dards. My search piv­oted, as it al­ways does, to un­der­stand those in­ven­tions. I ex­pected to be able to find the en­gi­neer or en­gi­neers names, track them down and in­ter­view them. It has worked around 100 times be­fore.

Jerry Morrison cre­ated IFF while work­ing at Electronic Arts and then went to Apple, where he li­aised with the AIFF team. I could eas­ily back­ground his work.

So I turned my at­ten­tion to TIFF, built ini­tially as an im­age stan­dard for desk­top pub­lish­ing. TIFF was able to store mono­chrome, grayscale, and color im­ages, along­side meta­data such as size, com­pres­sion al­go­rithms, and color space in­for­ma­tion. In many ways, it was a lot like AIFF so I was keen to know more. But I could­n’t find a TIFF cre­ator. No mat­ter how I en­quired, Aldus cre­ated TIFF.

To be clear, while a search for AIFF will of­fer up a com­pany (Apple) not a per­son, I was able to find Milne and Lentczner in part be­cause of their unique names and be­cause Apple pub­li­cised the AIFF work and those pub­li­ca­tions are archived.

All I had was Aldus, an American com­pany that cre­ated desk­top pub­lish­ing with the help of Apple and Adobe. In fact, Paul Brainerd, the co­founder of Aldus coined the term ‘desktop pub­lish­ing’ to quickly ex­plain the tech­ni­cal­ity of what they were do­ing to po­ten­tial in­vestors. But Aldus and their sem­i­nal prod­uct, PageMaker, are long gone, and there were no bread­crumbs for TIFF’s cre­ation.

Finally, af­ter a day-long trawl through MacWeek back is­sues, I found Steve Carlson. (below)

Then I ran a sim­i­lar length search through the Computer History Museum’s amaz­ing Oral Histories tran­scrip­tions. Brain­erd men­tioned Carlson’s name in an in­ter­view. (below)

But it was too brief an ex­pla­na­tion so I kept look­ing. Then the trail went cold.

And that was be­cause, folks had mis­spelt his name when quot­ing him and then that was copied into mag­a­zines, and re­views and so forth. Brain­erd’s CHM in­ter­view tran­script was wrong. But I did­n’t know that.

I just kept look­ing for Steve Carlson.

I found other in­ven­tors be­cause they had unique mid­dle or last names or by ran­dom meth­ods such as search­ing glider pi­lot li­cences in the Napa Valley af­ter a tip from a for­mer col­league that ‘so and so’ was a pi­lot in re­tire­ment. I had no tips, no links, noth­ing.

All the while, the an­swer was right un­der my nose. I had down­loaded the fi­nal Aldus TIFF spec­i­fi­ca­tions doc­u­ment, hop­ing to find the au­thor’s name. However, the name is seem­ingly writ­ten in white text on white pa­per - mak­ing it in­vis­i­ble. What?

See be­low where I have high­lighted the re­gion with a blue block over the text.

For a rea­son I can’t re­call, I down­loaded a plain text ver­sion and typed in Carlson to see if he was men­tioned, but I must have paused at ‘Carls…’ and the search func­tion­al­ity au­to­mat­i­cally filled in the rest. Suddenly I was star­ing at:

A quick trip to Google patents, and a search for Steve Carlsen, Stephen Carlsen. Bingo! Stephen E. Carlsen’s patents at Aldus (and Adobe) in Issaquah, WA.

I checked the ge­og­ra­phy, as most folks of a cer­tain age do not stray far from the ad­dresses filed in their patents, and typed Stephen’s cor­rectly spelled sur­name into the on­line US White Pages for Washington State. There was ‘a’ Stephen Carlsen listed in a re­tire­ment vil­lage in WA. His age matched, but there were no pub­lic fac­ing email ad­dresses.

I searched bul­letin boards on the topic of TIFF, as I had found a for­mer Apple en­gi­neer that way. Don had picked an ab­bre­vi­a­tion of his ini­tials and num­bers to post on BBS in his col­lege days and then car­ried that same com­bi­na­tion into adult­hood. Many of us did. I took a punt past­ing his unique pre­fix into hot­mail, gmail etc. and found Don and in­ter­viewed him, but - Stephen Carlsen did not show up in a BBS. So, no email to try.

My ‘last straw’ method for find­ing some­one is a stamped en­ve­lope. I wrote, printed and mailed a one-page let­ter to Stephen’s listed ad­dress, and crossed my fin­gers. Four months later he popped up in my email.

It was a sur­prise and a re­lief. We swapped a few emails, and he con­firmed the TIFF cat­a­lyst story. For Stephen it was ‘no big deal’. Once he had built the ini­tial TIFF, Aldus needed to con­vince 3rd party de­vel­op­ers and scan­ner man­u­fac­tur­ers to agree to TIFF as a stan­dard.

“We had to de­fine and pro­mote an in­dus­try stan­dard for stor­ing and pro­cess­ing scanned im­ages, so that we would­n’t have to write im­port fil­ters for every model of every scan­ner that would soon be en­ter­ing the bud­ding desk­top scan­ner mar­ket.”

Stephen him­self did much of the evan­ge­liz­ing as Paul Brainerd later pointed out:

“(Steve) de­vel­oped the stan­dard, and then we went out and pro­moted it in a se­ries of meet­ings with spe­cific com­pa­nies - as well as some work­shops we ran in Seattle and the Bay Area dur­ing the Seybold shows and the MacWorld shows.”

I sent Stephen a draft of what I had writ­ten and he sent a prompt re­ply say­ing - ‘Looks good’.

I fol­lowed up ask­ing him how he ended up at a tiny startup in Seattle called Aldus.

At that time, I was in­ter­view­ing for a graph­ics po­si­tion at Boeing Computer Services in Seattle, and no­ticed a small wanted ad that sounded re­ally in­ter­est­ing, and seemed to be an ex­cel­lent match for my back­ground and in­ter­ests. I in­ter­viewed with Paul and the 5-person mostly-ex-Atex en­gi­neer­ing team, and I was hired.

Out of cu­rios­ity I put Stephen’s email ad­dress, now that I knew it, into a Duck Duck search and found him help­ing peo­ple on­line with TIFF queries long af­ter Aldus had been ac­quired by Adobe. He also con­tributed to a Google Group called tiff­cen­tral.

Having in­ter­viewed so many peo­ple across more than a decade, I’ve got pretty good at judg­ing those who would like to talk or type, those who are ver­bose and those that are not. I knew Stephen had said what he was go­ing to say. I added his pi­o­neer­ing work on TIFF to the AIFF story and moved on.

Two years had flown by when I re­ceived an email yes­ter­day. His ex-wife Peggy found my pa­per let­ter and wrote to me. Stephen passed away ear­lier this year.

Thank you for your in­ter­est in and sup­port of Stephen’s bril­liant work cre­at­ing TIFF. I’m not sur­prised Stephen did­n’t fin­ish cor­re­spond­ing with you, as he had be­gun to strug­gle with us­ing his com­puter and phone. Some days were bet­ter than oth­ers for him, but he be­gan to lose touch with peo­ple dur­ing those months you were reach­ing out to him. He was a hum­ble man, and I guess never pushed to be rec­og­nized, al­though I be­lieve those who worked with him knew the truth. His last week was in my home, where he was never left alone.

Peggy fin­ished the email with, ‘I called him Mr TIFF up to his last mo­ment.’

The 10,000+ hours of book re­search dis­ap­peared in an in­stant. As sad as it was, I could see clearly that all of my work was worth it. Every sin­gle sec­ond. Because of this email.

Last night, as every­one in my house went to sleep, I took a deep breath and edited the Wikipedia page for TIFF, the Tag Image File Format.

It no longer reads ‘cre­ated by Aldus’, it reads ‘…cre­ated by Stephen Carlsen, an en­gi­neer at Aldus’

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con­nec­tion, dis­able any ad block­ers, or try us­ing a dif­fer­ent browser.

It is one of the most mys­te­ri­ous and, at the same time, best-known web­sites on the in­ter­net. Archive.today has built up a user base over a pe­riod of more than ten years who use the ser­vice to ac­cess pre­vi­ous snap­shots of a web page. So ba­si­cally like the Wayback Machine of the Internet Archive, only largely free of rules and pre­sum­ably there­fore also anony­mous. To the cha­grin of the me­dia in­dus­try, the ser­vice is also of­ten used to by­pass pay­walls. This is also pos­si­ble be­cause the ser­vice does not ad­here to com­mon rules and laws and of­fers no opt-out op­tion.

And so far, the op­er­a­tors have got­ten away with it. Although there have been mi­nor prob­lems in the his­tory of the ser­vice oc­ca­sion­ally, for ex­am­ple, a top-level do­main op­er­a­tor de­nied them fur­ther use of one of the many archive do­mains. However, the op­er­a­tion of the pro­ject, which is al­legedly fi­nanced by do­na­tions and own funds, was not se­ri­ously en­dan­gered.

But now the op­er­a­tors of archive.to­day are ap­par­ently fear­ing big­ger trou­ble. In re­cent months and years, they had be­come no­tice­ably qui­eter. Until two years ago, for ex­am­ple, ques­tions were reg­u­larly an­swered in the blog. In the of­fi­cial X ac­count, which had been silent for over a year, a new post ap­peared at the end of October new post. “Canary,” it said there, along with a URL. The men­tioned ca­nary bird is likely an al­lu­sion to an old cus­tom in min­ing. A ca­nary brought along warned the min­ers when it keeled over dead about the threat of in­vis­i­ble gas.

The deadly dan­ger that the site op­er­a­tors fear is ap­par­ently linked to the PDF linked in the X post linked PDF. It con­tains a court or­der that the US in­ves­tiga­tive au­thor­ity FBI has ob­tained. It in­structs the Canadian provider Tucows to hand over com­pre­hen­sive data about the cus­tomer be­hind archive.to­day. It con­cerns ad­dress and con­nec­tion data as well as pay­ment in­for­ma­tion. If Tucows does not pro­vide the data, penal­ties are threat­ened. Whether the court or­der is gen­uine and how the op­er­a­tors of the site ob­tained it could not be ver­i­fied so far.

Why the FBI is cur­rently in­ter­ested in archive.to­day, which is also ac­ces­si­ble un­der the do­mains archive.is and archive.ph, is not ev­i­dent from the court or­der. However, there are sev­eral ob­vi­ous start­ing points for in­ves­ti­ga­tions: in ad­di­tion to the ob­vi­ous rea­son of copy­right is­sues, the in­ves­ti­ga­tors could also be pur­su­ing sus­pi­cions about un­clear fi­nanc­ing, the ori­gin of the op­er­a­tors, or the tech­ni­cal ap­proach.

In 2023, Finnish blog­ger Janni Patokallio com­piled var­i­ous clues and re­search re­sults in a post in a post. According to this, Archive.today uses a bot­net with chang­ing IP ad­dresses to cir­cum­vent anti-scrap­ing mea­sures. There are also in­di­ca­tions that the op­er­a­tor(s) are based in Russia. Another pri­vate in­ves­ti­ga­tion from 2024 comes to a dif­fer­ent con­clu­sion. It names a soft­ware de­vel­oper from New York as the al­leged op­er­a­tor. According to this in­ves­ti­ga­tion, fol­low­ing the trail to Eastern Europe proved to be a red her­ring.

For New York’s No 1 Socialite, A. C.

1) Prioritize your ease of be­ing over any other con­sid­er­a­tion: par­ties are like ba­bies, if you’re stressed while hold­ing them they’ll get stressed too. Every other de­ci­sion is down­stream of your seren­ity: e.g. it’s bet­ter to have mediocre pizza from a happy host than fab­u­lous hors d’oeu­vres from a fraz­zled one.

2) Advertise your start time as a quar­ter-to the hour. If you start an event at 2:00, peo­ple won’t ar­rive till 2:30; if you make it 1:45, peo­ple will ar­rive at 2:00.

3) Invite a few close friends to come 30-60 mins ear­lier to set up / eat din­ner with you / hang out / what­ever, so that when the start time ap­proaches you’re al­ready hav­ing fun in­stead of stress­ing that no­body will come.

4) Most peo­ple will only go to a party where they ex­pect to know 3+ oth­ers al­ready.

5) Use an app like Partiful or Luma that shows the guest list to in­vi­tees. Start by invit­ing your clos­est friends, get some yesses, then ex­pand from there.

6) Send the in­vites in chat groups (or vis­i­bly cc’ed emails) to clus­ters of 4-5 peo­ple who know each other, so they can see that their friends are also go­ing.

7) When invit­ing peo­ple in­di­vid­u­ally, name­drop mu­tual friends who are in­vited or com­ing.

8) In a small group, the qual­ity of the ex­pe­ri­ence will de­pend a lot on whether the var­i­ous friends blend to­gether well. Follow your in­stinct on this, even if your in­stinct feels rude. It’s like cook­ing a dish, two in­gre­di­ents can each be fab­u­lous and still not go well to­gether.

9) A large party is more like an Everything Soup: you mainly need to avoid in­gre­di­ents that ruin the fla­vor for every­one else; be­yond that you can mostly throw in what­ever and see what works.

10) Regardless, try not to feel bad about not-invit­ing some­one if your heart says they would make the party less-fun for oth­ers. Make peace with gate­keep­ing be­cause if you don’t ex­clude a small % of peo­ple you will ul­ti­mately lose every­one else. Someone can be a good per­son and a bad fit for your party, so don’t think of it as a judge­ment on their soul. All of this is eas­ier in the­ory than in prac­tice.

11) Most events are bet­ter when roughly gen­der-bal­anced. Prioritize invit­ing peo­ple of the gen­der you’d likely have fewer of, then top up in­vites with the other. Once an event crosses a thresh­old (maybe 70%?) of male-or-fe­male dom­i­nance, most peo­ple of the other gen­der are likely to de­cline (or just not-come to your next party) as a re­sult. So there’s ul­ti­mately two equi­lib­ria, “roughly gen­der bal­anced” and “extremely un­com­fort­ably un­bal­anced,” and you need to stay in the at­trac­tion basin for bal­ance. To do this, keep your in­vite ra­tio at worst 60-40 in ei­ther di­rec­tion, in or­der to pre­vent a down­ward spi­ral.

12) Co-host par­ties with some­one you like a lot but who is­n’t in your ex­act so­cial cir­cle, so that your two friend-sets can in­ter­min­gle.

13) Figure out the flake rate in your so­cial cir­cles (the % of peo­ple who will RSVP yes and flake on the day), and set your in­vite num­bers with that in mind.  In my cir­cles, con­sis­tently 1/3rd of peo­ple who say they will be there will ac­tu­ally not.

14) Couples of­ten flake to­gether. This changes the prob­a­bil­ity dis­tri­b­u­tion of at­ten­dees con­sid­er­ably, and so your chance of los­ing a quo­rum in a small-group set­ting. Small-group cou­ple-events (e.g. 3-4 cou­ple din­ner par­ties) are very hard to man­age in a high-flake so­ci­ety, as a re­sult.

15) Create as much cir­cu­la­tion at your party as you can. People cir­cu­late more when stand­ing than when sit­ting, so try to en­cour­age stand­ing for those who can e.g. by hav­ing high-top ta­bles, or tak­ing away chairs from around ta­bles, or leav­ing shelves and counter-tops open for peo­ple to rest their plates and drinks.

16) Put the food in one part of the room and the drinks in an­other, or spread the food and drinks out around the space, so that peo­ple have lots of ex­cuses to move around the room.

17) If some­one ar­rives at your party and does­n’t know any­body, wel­come them and then place them with an­other group or per­son. Ideally you can pick some­one they’d specif­i­cally get along well with, at sec­ond-best just some­one who’s friendly and easy to talk to, but ul­ti­mately you can just in­sert them in any group that’s nearby and open. The main point is to pre­vent them hav­ing to butt in on strangers them­selves, which for many peo­ple is mor­ti­fy­ing, while your Host Privilege al­lows you to do it for them.

18) To leave a group con­ver­sa­tion, just slowly step back and then step away. Don’t draw at­ten­tion to your leav­ing or you’ll be pulled back in. It feels mildly weird to do this but it’s worth it.

19) Throughout the party, pri­or­i­tize in­tro­duc­ing peo­ple to each other and host­ing the peo­ple who are new or shy, even at the cost of get­ting less time hang­ing out with your best friends your­self. Parties are a pub­lic ser­vice, and the guests will (hopefully) pay you back for this by invit­ing you to par­ties of their own.

20) Let me re­peat that: Parties are a pub­lic ser­vice, you’re do­ing peo­ple a fa­vor by throw­ing them. Someone might meet their new best friend or fu­ture lover at your gath­er­ing. In the short term, lovely peo­ple may feel less lonely, and that’s thanks to you. In the long term, whole new chil­dren may ul­ti­mately ex­ist in the world be­cause you both­ered to throw a party. Throwing par­ties is stress­ful for most peo­ple, but a great kind­ness to the com­mu­nity, so gen­uinely pat your­self on the back for do­ing this.

21) The biggest prob­lem at many par­ties is an end­less es­ca­la­tion of vol­ume. If you know how to fix this, let me know.

It fi­nally hap­pened. Linux gamers on Steam as of the Steam Hardware & Software Survey for October 2025 have crossed over the elu­sive 3% mark. The trend has been clear for some­time, and with Windows 10 end­ing sup­port, it was quite likely this was go­ing to be the time for it to hap­pen as more peo­ple try out Linux.

As of the October 2025 sur­vey the op­er­at­ing sys­tem de­tails:

The snap­shot chart from our ded­i­cated Steam Tracker page shows the clear trend:

Overall, 3% might not seem like much to some, but again - that trend is very clear and equates to mil­lions of peo­ple. The last time Valve of­fi­cially gave a proper monthly ac­tive user count was in 2022, and we know Steam has grown a lot since then, but even go­ing by that orig­i­nal num­ber would put monthly ac­tive Linux users at well over 4 mil­lion. Sadly, Valve have not given out a more re­cent monthly ac­tive user num­ber but it’s likely a few mil­lion higher, es­pe­cially with the Steam Deck sell­ing mil­lions.

And if we look at the dis­tri­b­u­tion break­down chart from our page:

The over­all dis­tri­b­u­tion num­bers for October 2025:

The num­bers are still be­ing mas­sively pumped up by the Steam Deck with SteamOS Linux, which is not sur­pris­ing con­sid­er­ing that the Steam Deck is still in the top 10 of the global top sell­ers on Steam con­stantly. And with all the ru­mours and leaks sur­round­ing the up­com­ing Steam Frame, which will hope­fully be a SteamOS Linux pow­ered VR kit, we could see the num­bers just con­tinue to jump higher.

Democrat Zohran Mamdani has won New York’s may­oral race, NBC News pro­jects, af­ter the 34-year-old de­mo­c­ra­tic so­cial­ist en­er­gized pro­gres­sives in the city and across the coun­try while gen­er­at­ing in­tense back­lash from President Donald Trump and Republicans, as well as some Democratic mod­er­ates.

In his vic­tory speech af­ter van­quish­ing for­mer Gov. Andrew Cuomo, Mamdani claimed a broad man­date and set him­self up in di­rect op­po­si­tion to Trump, who made a late en­dorse­ment against him. “In this mo­ment of po­lit­i­cal dark­ness, New York will be the light,” Mamdani said.

“Together, we will usher in a gen­er­a­tion of change, and if we em­brace this brave new course, rather than flee­ing from it, we can re­spond to oli­garchy and au­thor­i­tar­i­an­ism with the strength it fears, not the ap­pease­ment it craves,” Mamdani said later, be­fore chal­leng­ing Trump di­rectly.

“This is not only how we stop Trump, it’s how we stop the next one,” Mamdani said. “So Donald Trump, since I know you’re watch­ing, I have four words for you: Turn the vol­ume up.”

A few months back, while play­ing around with , I came across some­thing that com­pletely de­railed my plans. Strange at­trac­tors - fancy math that cre­ates beau­ti­ful pat­terns. At first I thought I’d just ren­der one and move on, but then soon I re­al­ized that this is too much fun. When com­plex­ity emerges from three sim­ple equa­tions, when you see some­thing chaotic emerge into beau­ti­ful, it’s hard not to waste some time. I’ve spent count­less hours, maybe more than I’d care to ad­mit, watch­ing these pat­terns form. I re­al­ized there’s some­thing deeply sat­is­fy­ing about see­ing or­der emerge from ran­dom­ness. Let me show you what kept me hooked.

Most of what I’ve learned about strange at­trac­tors comes from work­ing on this vi­su­al­iza­tion. If you’re seek­ing ad­vanced math­e­mat­i­cal ex­pla­na­tions, this might not be for you. My in­ten­tion here is to share my learn­ings in an en­gag­ing and ac­ces­si­ble man­ner.

Dynamical Systems are a math­e­mat­i­cal way to un­der­stand how things change over time. Imagine you have a sys­tem, which could be any­thing from the move­ment of plan­ets to the growth of a pop­u­la­tion. In this sys­tem, there are rules that de­ter­mine how it evolves from one mo­ment to the next. These rules tell you what will hap­pen next based on what is hap­pen­ing now. Some ex­am­ples are, a pen­du­lum, the weather pat­terns, a flock of birds, the spread of a virus in a pop­u­la­tion (we are all too fa­mil­iar with this one), and stock mar­ket.

There are two pri­mary things to un­der­stand about this sys­tem:

Phase Space: This is like a big col­lec­tion of all the pos­si­ble states the sys­tem can be in. Each state is like a

snap­shot of the sys­tem at a spe­cific time. This is also called the state space or the world state.

Dynamics: These are the rules that takes one state of the sys­tem and moves it to the next state. It can be

rep­re­sented as a func­tion that trans­forms the sys­tem from now to later.

For in­stance, when study­ing pop­u­la­tion growth, a phase-space (world-state) might con­sist of the cur­rent pop­u­la­tion size and the rate of growth or de­cline at a spe­cific time. The dy­nam­ics would then be de­rived from mod­els of pop­u­la­tion dy­nam­ics, which, con­sid­er­ing fac­tors like birth rates, death rates, and car­ry­ing ca­pac­ity of the en­vi­ron­ment, dic­tate the changes in pop­u­la­tion size over time.

Another way of say­ing this is that the dy­nam­i­cal sys­tems de­scribe how things change over time, in a space of pos­si­bil­i­ties, gov­erned by a set of rules. Numerous fields such as bi­ol­ogy, physics, eco­nom­ics, and ap­plied math­e­mat­ics, study sys­tems like these, fo­cus­ing on the spe­cific rules that dic­tate their evo­lu­tion. These rules are grounded in rel­e­vant the­o­ries, such as Newtonian me­chan­ics, fluid dy­nam­ics, and math­e­mat­ics of eco­nom­ics, among oth­ers.

There are dif­fer­ent ways of clas­si­fy­ing dy­nam­i­cal sys­tems, and one of the most in­ter­est­ing is the clas­si­fi­ca­tion into chaotic and non-chaotic sys­tems. The change over time in non-chaotic sys­tems is more de­ter­min­is­tic as com­pared to chaotic sys­tems which ex­hibit ran­dom­ness and un­pre­dictabil­ity.

Chaos Theory is the sub branch of dy­nam­i­cal sys­tems that stud­ies chaotic sys­tems and chal­lenges the tra­di­tional de­ter­min­is­tic views of causal­ity. Most of the nat­ural sys­tems we ob­serve are chaotic in na­ture, like the weather, a drop of ink dis­solv­ing in wa­ter, so­cial and eco­nomic be­hav­iours etc. In con­trast, sys­tems like the move­ment of plan­ets, pen­du­lums, and sim­ple har­monic os­cil­la­tors are ex­tremely pre­dictable and non-chaotic.

Chaos Theory deals with sys­tems that ex­hibit ir­reg­u­lar and un­pre­dictable be­hav­ior over time, even though they fol­low de­ter­min­is­tic rules. Having a set of rules that gov­ern the sys­tem, and yet ex­hibit ran­dom­ness and un­pre­dictabil­ity, might seem a bit con­tra­dic­tory, but it is be­cause the rules do not al­ways rep­re­sent the whole sys­tem. In fact, most of the time, these rules are an ap­prox­i­ma­tion of the sys­tem and that is what leads to the un­pre­dictabil­ity. In com­plex sys­tems, we do not have enough in­for­ma­tion to come up with a per­fect set of rules. And by us­ing in­com­plete in­for­ma­tion to make pre­dic­tions, we in­tro­duce un­cer­tainty, which am­pli­fies over time, lead­ing to the chaotic be­hav­iour.

Chaotic sys­tems gen­er­ally have many non-lin­ear in­ter­act­ing com­po­nents, which we par­tially un­der­stand (or can par­tially ob­serve) and which are very sen­si­tive to small changes. A small change in the ini­tial con­di­tions can lead to a com­pletely dif­fer­ent out­come, a phe­nom­e­non known as the but­ter­fly ef­fect. In this post, we will try to see the but­ter­fly ef­fect in ac­tion but be­fore that, let’s talk about Strange Attractors.

To un­der­stand Strange Attractors, let’s first un­der­stand what an at­trac­tor is. As dis­cussed ear­lier, dy­nam­i­cal sys­tems are all about change over time. During this change, the sys­tem moves through dif­fer­ent pos­si­ble states (remember the phase space jar­gon?). An at­trac­tor is a set of states to­wards which a sys­tem tends to set­tle over time, or you can say, to­wards which it is at­tracted. It’s like a mag­net that pulls the sys­tem to­wards it.

For ex­am­ple, think of a pen­du­lum. When you re­lease it, it swings back and forth, but even­tu­ally, it comes to rest at the bot­tom. The bot­tom is the at­trac­tor in this case. It’s the state to­wards which the pen­du­lum is at­tracted.

This hap­pens due to the sys­tem’s in­her­ent dy­nam­ics, which gov­ern how states in the phase space change. Here are some of the rea­sons why dif­fer­ent states get at­tracted to­wards at­trac­tors:

Stability: Attractors are sta­ble states of the sys­tem, mean­ing that once the sys­tem reaches them, it tends to stay

there. This sta­bil­ity arises from the sys­tem’s dy­nam­ics, which push it to­wards the at­trac­tor and keep it there.

Dissipation: Many dy­nam­i­cal sys­tems have dis­si­pa­tive forces, which cause the sys­tem to lose en­ergy over time. This

loss of en­ergy leads the sys­tem to set­tle into a lower-en­ergy state, which of­ten cor­re­sponds to an at­trac­tor. This is

what hap­pens in the case of the pen­du­lum.

Contraction: In some re­gions of the phase space, the sys­tem’s dy­nam­ics cause tra­jec­to­ries to con­verge. This

con­trac­tion ef­fect means that nearby states will tend to come closer to­gether over time, even­tu­ally be­ing drawn

to­wards the at­trac­tor.

Some at­trac­tors have com­plex gov­ern­ing equa­tions that can cre­ate un­pre­dictable tra­jec­to­ries or be­hav­iours. These non­lin­ear in­ter­ac­tions can re­sult in mul­ti­ple sta­ble states or pe­ri­odic or­bits, to­wards which the sys­tem evolves. These com­plex at­trac­tors are cat­e­gorised as strange at­trac­tors. They are called “strange” due to their unique char­ac­ter­is­tics.

Fractal Structure: Strange at­trac­tors of­ten have a frac­tal-like struc­ture, mean­ing they dis­play in­tri­cate

pat­terns that re­peat at dif­fer­ent scales. This com­plex­ity sets them apart from sim­pler, reg­u­lar at­trac­tors.

Sensitive Dependence on Initial Conditions: Systems with strange at­trac­tors are highly sen­si­tive to their ini­tial

con­di­tions. Small changes in the start­ing point can lead to vastly dif­fer­ent long-term be­hav­iors, a phe­nom­e­non known

as the “butterfly ef­fect”.

Unpredictable Trajectories: The tra­jec­to­ries on a strange at­trac­tor never re­peat them­selves, ex­hibit­ing

non-pe­ri­odic mo­tion. The sys­tem’s be­hav­ior ap­pears ran­dom and un­pre­dictable, even though it is gov­erned by

de­ter­min­is­tic rules.

Emergent Order from Chaos: Despite their chaotic na­ture, strange at­trac­tors ex­hibit a form of un­der­ly­ing or­der.

Patterns and struc­tures emerge from the seem­ingly ran­dom be­hav­ior, re­veal­ing the com­plex dy­nam­ics at play.

You can ob­serve most of these char­ac­ter­is­tics in the vi­su­al­i­sa­tion. The one which is most fas­ci­nat­ing to ob­serve is the but­ter­fly ef­fect.

A but­ter­fly can flut­ter its wings over a flower in China and cause a hur­ri­cane in the Caribbean.

One of the defin­ing fea­tures of strange at­trac­tors is their sen­si­tiv­ity to ini­tial con­di­tions. This means that small changes in the start­ing state of the sys­tem can lead to vastly dif­fer­ent long-term be­hav­iors, a phe­nom­e­non known as the

but­ter­fly ef­fect. In chaotic sys­tems, tiny vari­a­tions in the ini­tial con­di­tions can am­plify over time, lead­ing to dras­ti­cally dif­fer­ent out­comes.

In our vi­su­al­i­sa­tion, let’s ob­serve this be­hav­ior on Thomas Attractor. It is gov­erned by the fol­low­ing equa­tions:

A small change in the pa­ra­me­ter a can lead to vastly dif­fer­ent par­ti­cle tra­jec­to­ries and the over­all shape of the at­trac­tor. Change this value in the con­trol panel and ob­serve the but­ter­fly ef­fect in ac­tion.

There is an­other way of ob­serv­ing the but­ter­fly ef­fect in this vi­su­al­i­sa­tion. Change the Initial State from cube to

sphere sur­face in the con­trol panel and ob­serve how the par­ti­cles move dif­fer­ently in the two cases. The par­ti­cles even­tu­ally get at­tracted to the same states but have dif­fer­ent tra­jec­to­ries.

The orig­i­nal “butterfly ef­fect” quote was coined by MIT me­te­o­rol­ogy pro­fes­sor Edward Lorenz in the 1960s. In 1961, Lorenz was run­ning a com­puter sim­u­la­tion of weather pat­terns us­ing a model with 12 vari­ables. He left to get a cup of cof­fee, and when he re­turned, he no­ticed that a tiny change in one of the ini­tial vari­ables (from .506127 to .506) had dras­ti­cally al­tered the en­tire weather pat­tern pro­duced by the sim­u­la­tion over the next two months. This led Lorenz to the pow­er­ful in­sight that small changes in a com­plex sys­tem can have large, un­pre­dictable con­se­quences .He called this the “butterfly ef­fect” - the idea that the flap of a but­ter­fly’s wings in Brazil could set off a tor­nado in Texas. Lorenz pre­sented his find­ings on the but­ter­fly ef­fect at a 1972 con­fer­ence, where the term was pop­u­lar­ized thanks to the metaphor of a but­ter­fly’s wings pro­vided by me­te­o­rol­o­gist Philip Merilees. You can play around with one of the at­trac­tors found by Edward Lorenz called the Lorenz Attractor in the vi­su­al­i­sa­tion.

This vi­su­al­iza­tion re­quired ren­der­ing a large num­ber of par­ti­cles us­ing Three.js. To achieve this ef­fi­ciently, we used a tech­nique called ping-pong ren­der­ing . This method han­dles it­er­a­tive up­dates of par­ti­cle sys­tems di­rectly on the GPU, min­i­miz­ing data trans­fers be­tween the CPU and GPU. It uti­lizes two frame buffer ob­jects (FBOs) that al­ter­nate roles: One stores the cur­rent state of par­ti­cles and ren­der them on the screen, while the other cal­cu­lates the next state.

Setting Up Frame Buffer Objects (FBOs): We start by cre­at­ing two FBOs, ping and pong, to hold the cur­rent and next state of par­ti­cles. These buffers store data such as par­ti­cle po­si­tions in RGBA chan­nels, mak­ing ef­fi­cient use of GPU re­sources.

Shader Programs for Particle Dynamics: The shader pro­grams ex­e­cute on the GPU and ap­ply at­trac­tor dy­nam­ics to each par­ti­cle. Following is the at­trac­tor func­tion which up­date the par­ti­cle po­si­tions based on the at­trac­tor equa­tion.

Rendering and Buffer Swapping: In each frame, the shader com­putes the new po­si­tions based on the at­trac­tor’s equa­tions and stores them in the in­ac­tive buffer. After up­dat­ing, the roles of the FBOs are swapped: The pre­vi­ously in­ac­tive buffer be­comes ac­tive, and vice versa.

1const cur­rent­Tar­get = flip ? ping : pong;2const next­Tar­get = flip ? pong : ping;3

4// Use cur­rent po­si­tions for cal­cu­la­tions in shader5u­ni­forms.po­si­tions.value = cur­rent­Tar­get.tex­ture;6

7// Render the other on the screen8gl.se­tRen­der­Tar­get(next­Tar­get);9gl.clear();10gl.ren­der(scene, cam­era);11gl.se­tRen­der­Tar­get(null);12

13flip = !flip;1const cur­rent­Tar­get = flip ? ping : pong;2const next­Tar­get = flip ? pong : ping;3

4// Use cur­rent po­si­tions for cal­cu­la­tions in shader5u­ni­forms.po­si­tions.value = cur­rent­Tar­get.tex­ture;6

7// Render the other on the screen8gl.se­tRen­der­Tar­get(next­Tar­get);9gl.clear();10gl.ren­der(scene, cam­era);11gl.se­tRen­der­Tar­get(null);12

13flip = !flip;

This com­bi­na­tion of ef­fi­cient shader cal­cu­la­tions and the ping-pong tech­nique al­lows us to ren­der the par­ti­cle sys­tem.

If you have any com­ments, please leave them on . Sooner or later, I will in­te­grate it with the blog. The hacker news dis­cus­sion can be found here.

Atuin re­places your ex­ist­ing shell his­tory with a SQLite data­base, and records ad­di­tional con­text for your com­mands.

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oha is a tiny pro­gram that sends some load to a web ap­pli­ca­tion and show re­al­time tui

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An ap­pli­ca­tion to man­age mark­down notes from your ter­mi­nal and com­pile them to HTML

It lets you quickly search through any kind of data source (files, git repos­i­to­ries, en­vi­ron­ment vari­ables, docker im­ages, you name it) us­ing a fuzzy match­ing al­go­rithm and is de­signed to be eas­ily ex­ten­si­ble.

A net­work di­ag­nos­tic tool that com­bines the func­tion­al­ity of tracer­oute and ping and is de­signed to as­sist with the analy­sis of net­work­ing is­sues.

Yōzefu is an in­ter­ac­tive TUI ap­pli­ca­tion for ex­plor­ing data of a Kafka clus­ter.

It is an al­ter­na­tive tool to AKHQ, Redpanda Console, or the Kafka plu­gin for JetBrains IDEs. It in­cludes a search query lan­guage in­spired by SQL, pro­vid­ing fine-grained fil­ter­ing ca­pa­bil­i­ties.