Loopdive is an AI first software company focusing on reinventing computing based on Natural Language.

München, Bayern
Joined September 2016
Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine. • Best-in-class fast local coding • Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark • Built from scratch in under 4 months Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.
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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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Loopdive retweeted
Javascript compiler JS² now covers 95% of ECMAScript 2015 unit tests #js #javascript #wasm #webassembly
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Loopdive retweeted
First level complete: ECMAScript 5 100% #js #javascript #wasm #webassembly
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Loopdive retweeted
First target in sight: 98.5% ECMAScript 5 tests pass. #js #javascript #wasm #webassembly
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Loopdive retweeted
2 more options for runtime eval side modules besides the basic interpreter: QuickJS interpreter (fast cold start) JIT compilation (fast on hot loops)
eval support is in – both for strings known at compile time (AOT compiled) and unknown at compile time (optional interpreter side module). passing 96% of eval tests in test262. #javascript #js #webassembly #wasm #ecmascript
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Loopdive retweeted
Playing „catch me if you can“ with V8’s JIT tier „Turbofan“ on npm packages 🐇 Speed relative to V8: Starting out at less than 10%, already seeing some 2-3 x speedups: 26% on cookie 18% on clsx 16% on acorn js2wasm.loopdive.com #javascript #js #webassembly #wasm
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Loopdive retweeted
90% ES5 unlocked: JavaScript to WebAssembly #javascript #js #webassembly #wasm
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Loopdive retweeted
eval support is in – both for strings known at compile time (AOT compiled) and unknown at compile time (optional interpreter side module). passing 96% of eval tests in test262. #javascript #js #webassembly #wasm #ecmascript
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Loopdive retweeted
JavaScript ahead-of-time compiler js2asm now passes 88.8% of official ECMAScript 5 test suite. js2.loopdive.com #javascript #webassembly #js #wasm
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js2wasm – JavaScript to WebAssembly AOT compiler: - ECMAScript 5 (ES5): 81.4% of official test suite passing - NPM Package acorn: 99.7% of unit tests pass js2.loopdive.com #javascript #webassembly #js #wasm @loopdive
JS² compiles JavaScript to WebAssembly ahead-of-time, sandboxing untrusted modules in your supply chain – running in JS engines or standalone – no interpreter or engine bundled. Goal: 100% ECMAScript & NPM compatibility; Method: spec/test-driven agile team of agents against spec + 100.000 unit tests, passing 75.4% of test262 in a JS host, 43.8% standalone. Module sandboxing limits blast radius for supply chain attacks or AI generated code Lightweight with no bundled interpreter or engine, starting at 100 bytes Fast, typically being an order of magnitude or more faster than interpreters or engines without JIT embedded in the module Garbage Collection natively supported by Wasm 3.0, builtin and robust instead of avoiding or embedding Help us bringing this vision of a new substrate for running JavaScript as lightweight, composable modules to life – by reviewing, testing, contributing, or donating! js2.loopdive.com @loopdive #JavaScript #TypeScript #WebAssembly #sandboxing #security
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Javascript/TypeScript to WebAssembly AOT compiler: ECMAScript 2026 test262: 70.8% JS host / 60.3% standalone npx js2wasm hello.js --target wasi wasmtime -W gc=y,function-references=y,tail-call=y,exceptions=y hello.wasm js2.loopdive.com #webassembly #wasm #javascript
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npm package acorn compiles and passes 3507/3518 99.69% of its test suite and produces byte identical ASTs to node-acorn
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