Pipelex (@PipelexAI) header
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Declarative language for composable Al workflows. Devtool for agents and mere humans.

Paris
Joined May 2025
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.
Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
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Something quietly important happened in agent tooling this week and it is not another model release. Tom Dorr posted about Pipelex, a project that lets you declare AI procedures in a typed file format called .mthds instead of writing free form prompts or brittle glue code. Each step is explicit, every input and output is typed, and the pipeline is deterministic outside the parts where you actually want the model to think. Agents write the .mthds file, a human reviews it, Pipelex runs it. That review step is the whole point. A dozen teams are independently converging on this same problem: agents need a format they can reliably generate and re-execute, not vibes based prompting that drifts every time you touch it. LangGraph solved a version of it with graph based state machines and passed CrewAI in GitHub stars this year on the strength of audit trails and rollback points for compliance heavy workflows. Temporal and Dagster solved an earlier version for plain software pipelines, durable execution that survives a crash halfway through. Atlassian shipped Agentic Pipelines inside Bitbucket this year for the same reason, chores nobody wants to hand babysit, wrapped in something repeatable. What is different about Pipelex is the packaging. MTHDS is proposed as an open standard, with a language for typed concepts and pipes plus a package system with lock files and a federated registry, the same shape as npm or PyPI but for AI methods instead of code libraries. I keep coming back to a pattern from the early API economy: nobody won by having the best individual endpoint, they won by having the best schema everyone else agreed to build against. Stripe won payments on developer experience and a stable contract, not on being the only company that could move money. The bottleneck agent tooling has right now is not capability, it is repeatability. My read is that whoever defines the format teams standardize on for declarative agent workflows ends up owning more of the stack than whoever ships the flashiest autonomous demo. teapaw.org/tom_doerr/status/21029…
Pipelex executes typed AI procedures declared in .mthds files. github.com/Pipelex/pipelex
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Pipelex retweeted
Build and run AI methods as executable pipelines. github.com/Pipelex/pipelex
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Replying to @garrytan
Go beyond skills: with MTHDS you get deterministic orchestration of multiple AI jobs + structured generation and composability. It's the missing layer between Skills and Code. It installs into Claude Code with a simple copy-paste, just like gstack --> github.com/Pipelex/pipelex
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4 months of heads-down building. Biggest Pipelex release yet. New open language spec called MTHDS. A Claude Code plugin that writes, tests, and visualizes AI workflows for you. One API key for all models. A package manager to share it all. Full drop 🧵
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Without Know-How, your agent is a zombie 🧟‍♂️. A Knowledge Graph isn't enough. → We need a Know-How Graph ‼️ ⏩ A network of executable methods that AI can actually reuse. Read our Viewpoint 👇 ➡️ knowhowgraph (dot) com
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Replying to @PipelexAI
@PipelexAI Listen to @lchoquel talk about how Pipelex will change building of AI workflows forever, on Founder Journeys Pod on Spotify for Creators open.spotify.com/episode/6KX…
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New Episode! Presenting Pipelex, The open standard language simplifying AI workflows for everyone! youtu.be/KhIfkK5sIRM @lchoquel @PipelexAI
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Pipelex retweeted
9pm Wednesday at @PipelexAI evening hackathon I was building invoice OCR recognition tool Among demos - Zillow listing scam detector, calorie counter and Free Food events finder in #SanFrancisco
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First Pipelex hackathon in SF last night ⚡ 50+ builders. 4.5 hours. Dozens of AI workflows built from scratch. The energy was unreal 🧵
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AI that builds AI 🌀 We can vibe-code entire apps in 4 prompts, but still spend days writing custom code for one AI workflow that barely hits 80% reliability. That's backwards. So we fixed it.
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AI doesn't just need agents - it needs workflows. 🚀 Today we're launching Pipelex: an open-source DSL + Python runtime for repeatable AI workflows. Think Dockerfile/SQL for LLM pipelines - declare steps, swap models, let the runtime handle execution.
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🚀 Hey builder! Glad to tell you that today we're launching @PipelexAI : an open-source DSL + Python runtime for repeatable AI workflows. AI doesn't just need agents - it needs workflows. Most knowledge work follows patterns. If you know the path, you don't need an expedition… you need a bullet train. Think Dockerfile/SQL for LLM pipelines - declare steps, swap models, let the runtime handle execution. What makes it different: Declarative, not glue code Agent-first: LLMs can run, audit & build workflows (via MCP) Open & composable: MIT license, works with any provider What's included: → Python runtime → FastAPI server + Docker → MCP server (agent integration) → n8n node → VS Code extension I would love your feedback, don't hesitate to test it and tell me. (links in bio)
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[LAUNCH WEEK - day #1] AI doesn't just need agents - it needs workflows. Most knowledge work follows patterns. If you know the path, you don't need an expedition… you need a bullet train. 🚀 Today we're launching @PipelexAI : an open-source DSL + Python runtime for repeatable AI workflows. Think Dockerfile/SQL for LLM pipelines - declare steps, swap models, let the runtime handle execution. What makes it different: Declarative, not glue code Agent-first: LLMs can run, audit & build workflows (via MCP) Open & composable: MIT license, works with any provider What's included: → Python runtime → FastAPI server + Docker → MCP server (agent integration) → n8n node → VS Code extension Try it, break it, tell us what hurts. Feedback = gold. (links in bio)
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→ @PipelexAI is a declarative language and Python runtime for repeatable, agent-first AI workflows. It defines LLM pipelines like Dockerfile or SQL — describe steps and interfaces, and any model or provider can execute them. 🔴 Live on Dev Hunt → devhunt.org/tool/pipelex
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Pipelex hasn't launched any cryptocoin If you see a Pipelex AI cryptocoin, it's a scam
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AI Isn't Real Intelligence? Bullshit.
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open-source tool for building repeatable AI workflows with modular, reusable components
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