✍️ on tech, biz, and ai. Bootstrapping @RarelyDecaf token-by-token, previously VC-backed generalist. Dad. Remote-native. Regularly in 🇲🇽

Joined April 2011
I read this saga as: a) The CRUD app + database layer is commoditizing fast. b) Differentiation is everything else: reputation, networks, data that compounds. For small teams, I think compounding data (basically structured markdown around the problems and solutions of one ICP) is the easiest one to focus on. One thing I’ll shout into the void for indies feeling hopeless: stop competing on features. Start delivering a service through agents. Build a custom harness, get clear on the service you deliver, and let your advantage accumulate there.
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Great analogy. Also, I think this is a good reminder for anyone struggling to think through differentiation and competition in the age of ai. IMO, you *have to* build a custom agent harness/loop into your product to keep up because that's where you'll differentiate.
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Well-articulated post on where we are with AI. Also a good reminder to just ask people whether (and how) they use AI day to day. Wherever you are on the curve, you’ll probably be surprised how different everyone else is.
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
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Great analogy. Also, I think this is a good reminder for anyone struggling to think through differentiation and competition in the age of ai. IMO, you *have to* build a custom agent harness/loop into your product to keep up because that's where you'll differentiate.
.@OpenRouter co-founder Alex Atallah on the "everyone is building the same thing" take: "This reminds me of this tweet I saw... Everybody's building an agent loop with notifications, third-party connectors, context management, memory, sandboxes, agentic web search, an always-on agent on top." "This product is showing up everywhere. It is showing up everywhere, but these are just the new table-stakes primitives." "A 2005 version of that tweet would be: 'Oh, everybody's building the same thing. A database, a users table, a sign-in page, a sign-up page, a profile page, a logout page. Everything's the same.'" "There's like a lot of differentiation really. There's table-stakes needs for AI just like there's table-stakes needs for the web." @alexatallah @amasad
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So much opportunity in building custom ai harnesses. Pi's 1.0 release is a good excuse to try building yours.
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Crazy progress in 3.5 years. The Will Smith eating spaghetti meme was March 2023.
A robot in Dunder Mifflin
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Wow—pretty incredible that Claude, OpenAI, and xAI are all down. I wonder what's causing this.
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So many startups are selling "AI employee for X" in various digital and professional services. Feels like that will do well in the short-term. But long-term, I keep thinking about this—how the real innovation and gains will come from those who completely rethink delivery.
Replying to @caffeinatedwes
I like the electrification metaphor here. When electricity hit factories in the 1880s, owners swapped central steam engines for electric motors and kept the same layout. Economists agree that productivity gains didn't show up for roughly 30 years. The breakthrough came from redesigning the entire factory, not from a better motor.
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And for what it's worth—rethinking delivery isn't just "agents do the work." That part's coming either way. It's the UX. Right now the AI-pilled have a new job: managing agents and managing agent sessions. You open a dozen or so chats and hold the actual projects and their states together in your head. That's the driveshaft anecdote again. The interface arranged around where the power is, not around the work. But, most work is a small set of recurring shapes. For example, intake, review, report, handoff. Build for those and the project stops living in your head. Your interface for the project is the project.
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Fascinating. 2013-ish Airtable and Bending Spoons were both founded 2021 - AT raised $735m at $11B valuation, estimated 70x revenue. Founders maybe have 15%-20% of company. - BS had no vc, closed at 109 million euros in revenue. 100% founder owned. 2026 - BS goes public at $18.4 billion. Founders outcome at ~$2b pp and 83% control of co. - BS purchases AT and AT founder maybe at $200m to $450m net worth
We've officially agreed to acquire Airtable for $1.285B! 😍
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Markdown-as-kanban is one of the highest-leverage patterns I've found recently for working with agents. The .md file is the perfect interface for agents. A rendered view is the perfect interface for humans. I run all of @RarelyDecaf's work this way. Clients see a simplified rendered view of the board. I see a more ops-focused view of links. And agents see and maintain it all. One file, visible 3 ways. Inspired by @mattshumer_'s workbench.md and @elirousso's autobahn.run
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Feels like the "build vs buy" with AI harnesses is not necessarily build (ie, Pi) or buy (ie, Claude Code) wholesale, but decomposing into jobs to be done. Silly to build a coding harness. Probably less silly for consulting.
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This is sad. I was making so much progress with this model. That said, there's no keeping this level of intelligence in a box for long. Sci-fi has really become reality in a few short years.
The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees. The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance. Access to all other Claude models is not affected. We apologize for this disruption to our customers. We believe this is a misunderstanding and are working to restore access as soon as possible. Read our full statement: anthropic.com/news/fable-myt…
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The leap to Opus 4.5 was insane. The leap to Fable feels just as insane. A thought I keep coming back to: it's so important to work toward healthy financials as an entrepreneur starting out, because that's what lets you actually use these models. Yes, open source is improving and intelligence is getting cheaper. But the real shift is going from "oh man, $200/month" and debating Claude plans... to seeing a machine: dollars in, more dollars out. The spend goes into your system and compounds. Same rule as always: you grow to afford the thing that grows you. It used to be people and still is for most businesses. But I feel like for the successful companies of the next decade, it's going to be compute.
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A great reason for you to use an LLM provider-agnostic harness is the ability to switch between the right model for the job. We switched to DeepSeek v4 Pro everywhere we were using GPT 5.5 low a few weeks ago. (we still use Opus, Sonnet, GPT 5.5 xhigh) Great performance for <1/10 of the cost of using GPT 5.5 low
Pulled the trigger today and switched 100% of Lindy traffic to DeepSeek v4, churning from Anthropic models. Saves us millions of $ and we're actually seeing an *increase* in performance on many core use cases. Transformative for the business.
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There's a lot of conversations debating whether AI is too expensive or whether you just need to use more of it. This argument has played out before with every major technology. And both sides are usually off in the same way.
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I've been going through this in my own consulting practice. For the first two years or so, I added AI tools to existing workflows. It helped and was incredible. But the productivity gains also surfaced the real bottlenecks: - context loss between handoffs - coordination overhead - the telephone game of intent. The workflow itself was the problem, not the tools.
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Most operational complexity exists to move knowledge between humans. If intelligence is cheap and everywhere, a lot of that complexity just isn't necessary. The question then shifts from "Where do I plug in AI?" to "What does this look like designed from scratch?"
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