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Ellf adds annotation, execution and project guidance to NLP/ML coding assistants

An Ellf developer says computation runs on user-controlled infrastructure and user data is never sent to Ellf.

Matthew HonnibalMH
1 Source, 3h ago, first seen 3h ago

TLDR

An Ellf developer says the tool works with coding assistants such as Claude Code and Codex, adding human annotation, user-controlled computation and a knowledge base built from 10-plus years of advice. Users can run processing on a Kubernetes cluster or local Linux machine, the developer says. In the October 10, 2026, announcement, they said registrations would open the following week and offered early access through an invite code.

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1 Source, first seen 3h ago

13 likes7 comments6 saves2 reposts

Combined views

989

1 Source, first seen 3h ago

13 likes7 comments6 saves2 reposts

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1 Source

Matthew Honnibal@honnibalI haven’t been posting here much but we’re finally releasing an NLP/ML devtool we’ve put a lot of work into. You know how there are tools for agentic coding of websites? Ellf is like that for NLP or ML models. It works with coding assistants like Claude Code, Codex etc and adds the missing pieces needed to make development of NLP or other ML models much better. Ellf does this by providing three pieces Claude Code etc are missing for NLP: annotation, execution and knowledge. The annotation component is key because it lets the agent schedule a little bit of computation on the human. You don’t need to create a massive dataset. Just annotating a few hundred examples here or there helps massively to evaluate, fine-tune and plan. The problem most teams face is they don’t have a low-friction way to get this work done. We’ve always believed in annotation tooling that scales down to low-volume use-cases, which is why we emphasised flexibility and programmability in Prodigy. Coding assistants really unlock this flexibility, because the agent is able to assemble an annotation experience that’s as efficient as possible for your exact use-case. The next thing we wanted to solve with Ellf is execution. Doing NLP/ML projects means code+data development. This is something I think MLOps tools mostly get wrong, because they’re so overly focussed on hyper-parameter search, which is such a small part of what you’re ever doing. Mostly you need to run a lot of once-off scripts to manipulate data, and even when you’re doing experiments, you’re mostly trying to answer some specific question where you have to do a lot of legwork to set up a direct comparison. Ellf gives your coding assistant a seamless and flexible way to perform that computation. Obviously Claude Code can build you pretty much anything by itself — Bash is fully general — but if you make it solve “run this in the cloud” as a subtask in your session, it’s going to do worse at the actual problems you’re trying to solve. Importantly the computation runs under your control. It’s a bit like plugging in self-hosted workers for something like Github Actions. You run a Kubernetes cluster (which can just be local Linux machine if you want) and this is where all the data processing and computation happens. Your data is never sent to us. The final part is knowledge. Out of the box, coding assistants generally put NLP solutions in the too-hard basket, and suggest just using an LLM with some in-context learning. They won’t suggest doing annotation, and they don’t know how to structure a project. A little bit of correction goes a long way on this. We’ve built a knowledge base with our 10+ years of advice, across our forums, talks, blog posts and documentation. Alongside some carefully designed skills, you can use Ellf to plan out your project, and it will do a much better job of recommending you approaches than a coding assistant will do by default. We’re opening registrations for Ellf next week, but if you’ve read this far, you can get early access with the invite code HONNIBAL-TWITTER on app(dot)ellf(dot)ai . I’ve been pretty down on this platform for some time now so I want to see whether I should suck it up and post here more.3h
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    Matthew Honnibal@honnibalI haven’t been posting here much but we’re finally releasing an NLP/ML devtool we’ve put a lot of work into. You know how there are tools for agentic coding of websites? Ellf is like that for NLP or ML models. It works with coding assistants like Claude Code, Codex etc and adds the missing pieces needed to make development of NLP or other ML models much better. Ellf does this by providing three pieces Claude Code etc are missing for NLP: annotation, execution and knowledge. The annotation component is key because it lets the agent schedule a little bit of computation on the human. You don’t need to create a massive dataset. Just annotating a few hundred examples here or there helps massively to evaluate, fine-tune and plan. The problem most teams face is they don’t have a low-friction way to get this work done. We’ve always believed in annotation tooling that scales down to low-volume use-cases, which is why we emphasised flexibility and programmability in Prodigy. Coding assistants really unlock this flexibility, because the agent is able to assemble an annotation experience that’s as efficient as possible for your exact use-case. The next thing we wanted to solve with Ellf is execution. Doing NLP/ML projects means code+data development. This is something I think MLOps tools mostly get wrong, because they’re so overly focussed on hyper-parameter search, which is such a small part of what you’re ever doing. Mostly you need to run a lot of once-off scripts to manipulate data, and even when you’re doing experiments, you’re mostly trying to answer some specific question where you have to do a lot of legwork to set up a direct comparison. Ellf gives your coding assistant a seamless and flexible way to perform that computation. Obviously Claude Code can build you pretty much anything by itself — Bash is fully general — but if you make it solve “run this in the cloud” as a subtask in your session, it’s going to do worse at the actual problems you’re trying to solve. Importantly the computation runs under your control. It’s a bit like plugging in self-hosted workers for something like Github Actions. You run a Kubernetes cluster (which can just be local Linux machine if you want) and this is where all the data processing and computation happens. Your data is never sent to us. The final part is knowledge. Out of the box, coding assistants generally put NLP solutions in the too-hard basket, and suggest just using an LLM with some in-context learning. They won’t suggest doing annotation, and they don’t know how to structure a project. A little bit of correction goes a long way on this. We’ve built a knowledge base with our 10+ years of advice, across our forums, talks, blog posts and documentation. Alongside some carefully designed skills, you can use Ellf to plan out your project, and it will do a much better job of recommending you approaches than a coding assistant will do by default. We’re opening registrations for Ellf next week, but if you’ve read this far, you can get early access with the invite code HONNIBAL-TWITTER on app(dot)ellf(dot)ai . I’ve been pretty down on this platform for some time now so I want to see whether I should suck it up and post here more.3h
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