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You use Claude every day for work, so it is worth understanding why it sometimes throttles you when OpenAI's tools do not, and whether that changes soon. A Reddit post making the rounds lays out the money behind the two companies' different bets.

The Wall Street Journal reported that OpenAI now expects to spend $750 billion on infrastructure, the chips and buildings needed to run its models, by 2030. That is 25 percent more than the estimate it gave earlier this year. Part of that is a single data center in Georgia built to use 3.2 gigawatts of power, at a cost of $30 billion. OpenAI's valuation is now approaching $1 trillion ahead of a planned IPO, when a company first sells shares to the public. The scale looks extreme, and the poster admits that if OpenAI is right about demand, that spending may look justified in hindsight.

Anthropic, Claude's maker, is taking a different approach. Instead of building its own giant data centers, it is leasing computing power through a set of deals: SpaceX, Amazon, Microsoft, Google, and more recently Meta and AMD. Its one direct build is a $50 billion deal with a company called Fluidstack for data centers in Texas and New York, small next to what OpenAI is spending. The practical result, according to the poster: Claude users hit usage limits and outages more often than users of OpenAI's tools. When Anthropic released its Fable 5 model, it was available only to Max and Team Premium subscribers, at half the usual weekly usage limit, which the poster reads as a sign of a chip shortage, not a pricing choice.

An investor's line captures the stakes: Apollo's head of thematic investing said that in a world where chips are scarce, having enough access to them becomes the reason a rival cannot easily copy what you do. Owning your own computing power gives you control over your own roadmap. The poster adds a caution from an analyst they trust: both companies may be burning cash faster than revenue can cover, and nobody will know who made the right bet until IPO filings force real numbers into the open.

This names exactly the shift you are already living through with Claude Code and your own agent fleets: the skill that matters now is not writing a good prompt, it is designing the system that keeps prompting the agent for you.

The post, from a builder posting as @0xwhrrari on X, puts it directly: "You should not be prompting coding agents anymore. You should be designing loops that prompt your agents." Boris Cherny, who leads Claude Code at Anthropic, said the same thing in his own words: "I do not prompt Claude anymore. I have loops running that prompt Claude and figure out what to do. My job is to write loops."

The old way is you type one task, wait for one answer, review it yourself, fix the mistake yourself, then prompt again, so you are still the loop. The new way is you set the goal, and the system takes it from there. It works out what is needed (discover), plans the work (plan), has the agent do it (execute), has a checker confirm the result (verify), and sends it back around if it fails (iterate). If it passes, it ships.

The size of the loop matters. A single-agent loop is one agent running that whole cycle itself, good for a focused job like a bug fix or a research summary. A fleet loop is bigger: one orchestrator agent, the one deciding which step runs when, breaks the goal into pieces and hands them to specialist agents, each of which can spin up smaller helper agents of its own. That is closer to a small team running a project start to finish than one person editing their own draft.

There is a real cost to this and the post is upfront about it. A single medium coding loop can burn 50,000 to 200,000 tokens, chunks of text, roughly three quarters of a word each. A fleet loop with an orchestrator and several specialists can run 500,000 to 2 million tokens, and a loop scheduled to run every day can reach millions of tokens in a week. That is why the post argues for starting with closed loops, ones where you set the steps, a check after each step, and a clear stop condition, rather than open loops that let the agent search freely and can drift, burn tokens, and produce messy results. Open loops are worth trying once your checks are solid enough to catch what goes wrong.

The post also lists what a working loop needs: something that starts it automatically on a schedule or a trigger so you do not have to remember to run it, separate workspaces for each agent so two agents editing the same project do not overwrite each other, and a written file with your project's vision, rules, and build and test steps, so every loop starts with that context already loaded instead of starting cold.

This is close to the agent fleet model you already run across your own projects, so it is worth seeing what a well built version of the idea looks like elsewhere. Anthropic has quietly open sourced a repo that turns Claude into a set of ready made office roles: a sales rep, a marketer, a financial analyst, a legal reviewer, a data analyst, and more. Each role comes pre loaded with the workflows, the domain knowledge, and the tool connections that job actually needs, so you are not prompting from scratch each time, you are installing a specialist who already knows the work.

Each plugin is built from three pieces. Skills are the domain knowledge and best practices for that role, and Claude pulls them in automatically when they are relevant, you never call them by name. Commands are ready made workflows you trigger with a slash, like /sales:call-prep to get a full pre call brief from just a company name, or /data:write-query to turn a plain description into working SQL (the language used to query databases). Connections are the outside tools that role plugs into: the sales plugin reaches into your CRM (the software that tracks customers and deals), the finance plugin reaches into your data warehouse, the marketing plugin reaches into Canva and your analytics dashboards. This three piece structure, the same one Anthropic used to build its paid Claude for Legal and Claude for Financial Services products, is what you are getting here for free.

The repo ships a full roster and you install only what you need. Productivity handles tasks, calendars, and daily routine, and plugs into Slack, Notion, Asana, Linear, Jira, and Microsoft 365. Sales does account research, call prep, pipeline tracking, and cold outreach, plugged into HubSpot, Close, Clay, ZoomInfo, and Fireflies. Marketing covers content, campaigns, brand voice, competitor sweeps, and SEO audits, plugged into Canva, Figma, HubSpot, Klaviyo, and Ahrefs. Customer support handles ticket triage and turns solved tickets into help center articles, plugged into Intercom, HubSpot, and Guru. Product management covers specs, roadmaps, and user research, plugged into Linear, Figma, Amplitude, and Pendo. Finance handles journal entries, reconciliations, statements, and month end close, plugged into Snowflake, Databricks, and BigQuery. Legal handles contract review and NDA triage, plugged into Box, Egnyte, and Microsoft 365. Data writes queries and checks results before you publish them, plugged into Snowflake, Databricks, BigQuery, and Hex.

Setup is a short, fixed sequence. Download Claude Desktop from claude.com/download and open the Cowork tab, the mode where Claude can touch real files and tools instead of just chatting. Add the plugin marketplace with one command in Cowork's built in terminal, a one time step. Install the role you want most, for example sales, and it activates the moment it lands, no separate setup. It works standalone from day one: paste your notes, upload a spreadsheet, describe the situation, or run a slash command and it hands back a finished output without you first explaining what a good one looks like. The real jump comes next: open Connectors and authorize the actual tools that role uses. Standalone is the intern, guessing from what you paste in. Connected is the senior hire, pulling real pipeline data, reconciling against real numbers, building reports from live analytics instead of asking you to paste them in first.

Install two or three roles and they work together in the same session: a data role pulls the numbers, a finance role reconciles them, a marketing role turns the result into a report, one operator running a small cross functional team with no extra payroll. The repo also ships a meta plugin built specifically to reshape any role around your own tools, your own terminology, and your own process. For your own agent roles, the part worth stealing is the three way split baked into every plugin here: knowledge the model reaches for on its own, workflows you name and trigger, and tools it uses only once you explicitly connect them.

Outside your usual reading, but there is a real thread here: developers keep making an old, cheap Kindle do more with the right software, the same spirit behind a lot of your own tinkering. If you jailbroke a Kindle, meaning you removed the restrictions Amazon puts on it, to run Tailscale, a tool that links your own devices into one private network called a tailnet, you already had something useful. A new update pushes it further, adding real networking instead of just a device that shows up on the map.

The earlier version, built by developer Mitanshu Sukhwani, got the Kindle onto your tailnet: it showed a green dot in Tailscale's web console, you could reach it by its Tailscale address, and you could SSH in, meaning open a remote command line, to keep tinkering. But being reachable is not the same as routing all your traffic through the network. A jailbroken Kindle is forced to run Tailscale in what is called userspace mode, meaning it cannot use the device's own network routing layer, called TUN mode. So an ereader app like KOReader could ask the Kindle to reach another Tailscale device, say a Calibre ebook server at its Tailscale address, and the request would simply fail: the Kindle had no way to route it, and the connection dropped. Reading and networking lived in two separate worlds on the same device.

An update to the Kindle's KUAL app by developer greywolf1499 closes that gap with two new modes. The first is a proxy mode: you point an app's proxy settings at 127.0.0.1:1055, and the Tailscale background process listening on that port routes the connection through the tailnet on the app's behalf. It offers both SOCKS5 and HTTP CONNECT versions for apps that prefer one or the other, using port 1056 for the second. The bigger change is a full TUN mode that, on some Kindle models, makes Tailscale work at the device level the way it does on a phone or laptop, with no proxy needed. On top of both, Tailscale SSH is now on by default, which replaces the old USB networking SSH setup and its exposed, well known default username and password, a real gap on any device you are opening up to a network.

Once an app is proxied through the tailnet, the practical list grows fast. You can point KOReader, a popular ereader app, at your own Calibre or Wallabag server to sync books and saved articles, connect to an Audiobookshelf server for audiobooks, use an app called Readest to keep reading progress in sync across devices, or link KOReader's RSS reader to a self hosted feed server. You can even load simple dashboards in the Kindle's famously slow browser, or pair a Bluetooth keyboard with the kterm terminal app to SSH into other machines on your network, mostly for the novelty of doing it from an e-reader. A separate, simpler option exists too: a Tailscale plugin built directly for KOReader, which sets up the same proxy automatically and also works on Kobo and PocketBook e-readers, tested on Kindle models PW5 and PW6.

None of this required Amazon's blessing, a factory reset, or new hardware, just a jailbroken Kindle and a community of people documenting each step on GitHub. It is a small, concrete case of the same instinct behind a lot of your own setups: an old, weak device plus the right software stack is still worth more than the device alone.

Outside your usual reading: a change of pace from AI news, and a good reminder that resourceful builders have always made powerful things with almost nothing. The demoscene is a decades old subculture of self taught coders and artists, mostly teenagers in the 1980s and 90s, who competed to squeeze real time graphics and music out of home computers like the Commodore Amiga. With no budget, no formal training, and often no manuals, they built their own tools from scratch, and those tools ended up as strange as the art itself.

The best example is Elite Sinus Producer, built by a coder called Ipec Elite. Amiga demos are called real time because the effects are computed frame by frame in code, but the trick that makes this possible on a 7 megahertz chip, extremely slow by today's standards, is precalculation: instead of doing the math live, you calculate curves like sine waves once, ahead of time, and store the results in a lookup table the demo reads from as it runs. Elite Sinus Producer exists purely to generate those tables. Its main menu is chosen with the F-keys, and every selection plays a loud sample of a cuckoo clock. Picking the tool's Flower option draws a curling, ornamental path meant as a route for a sprite, a small animated graphic, to travel along, and the result saves out as raw assembly source code, ready to drop into a demo. Its help screen is nearly unreadable: moving blue bars behind the text and a background that flashes red and cyan, set in a font its own author says was designed purely for legibility.

The coding tools were just as improvised. Amiga sceners mostly used Seka, a commercial assembler, a program that turns human readable code into instructions a chip runs directly, that was picked apart and rebuilt so many times, including a spinoff called AsmOne, effectively Seka Updated, that its family tree of forks and hacks rivals that of Unix itself. One especially useful category was the ripper: a tool for combing through a computer's memory after you quit a game, hunting for leftover sound samples, sprites, or whole music tracks worth reusing. A second, more specific ripper existed only to search memory for lost Seka source code after a crash, because the Amiga had no memory protection at all and demo coding crashed constantly. Coders were told to save often, and mostly did, but when someone forgot, a warm reboot and this ripper could sometimes pull the code back out of live memory before it was gone for good. A separate tool called The Sinus Creator did the same lookup table job as Elite Sinus Producer through a plainer two window text interface, saving its output as a Seka file.

Music worked the same improvised way. A tracker lets a musician lay out notes and effects across a grid, one column per sound channel, closer to a programming editor than to sheet music, while also managing the sampled or synthesized instruments underneath. Every Amiga sample based tracker traces back to one commercial program, Karsten Obarski's Ultimate Soundtracker from 1987; sceners took it apart and rebuilt it as NoiseTracker, which was rebuilt again into ProTracker, which then splintered into more hacked versions than anyone can fully map. The format itself echoes something even older: SoundMonitor 1.0 for the Commodore 64, written by Chris Huelsbeck, not a scene release itself but the program whose one track per channel layout inspired Ultimate Soundtracker. NoiseTracker itself, built by the Swedish duo Mahoney and Kaktus, was not the first tracker but became the most popular, and its design choices still shape how tracker software works today, on the Amiga and well beyond it.

Nothing here ran on a budget bigger than a teenager's bedroom computer. The tools were rebuilt from someone else's commercial software, taken apart, hacked, and passed hand to hand, which is a fair description of how a lot of the best small scale software has always gotten made.

A LinkedIn roundup ranks 20 open source AI repos by GitHub stars: coding agents like OpenClaw (278,000 stars) and Claude Code (75,000), tools like Firecrawl (89,000), and infrastructure like Open WebUI (126,000). A fast way to see which tools are actually gaining traction with other builders, worth a scan next time you want to try something new.