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As engineering, product, design, DS, etc. melt into a new kind of role, I was reflecting on what roles might look like in the future. For example, when I look at the Claude Code team I see what I think is five archetypes:
1. Prototyper: comes up with brand new ideas; churns out many ideas, most of which don't ship
2. Builder: quickly turns a prototype/idea into production-grade product/infra
3. Sweeper: cleans up the UI, simplifies the code and system, unships, optimizes performance
4. Grower: takes a product that has been built and iterates on it to improve Product-Market Fit
5. Maintainer: owns a mature system to make it secure, reliable, fast, and efficient as it scales
Many people span across 2 roles, and sometimes 3 roles. I also notice that these roles are not really tied to job function -- eg. across Anthropic, some designers match category 1, some 2, some 3; same for engineers, PM, DS.
A healthy team needs a mix of these, depending on the product:
- A product that is new and pre-PMF needs people that are strong at 1+2+3
- A product that is growing and has found PMF needs 2+3+4 and some 5
- A product that has strong PMF needs 3+4+5 and some 2
Maybe product roles of the future will look more like this, and less like the domain-specific roles of today?
introducing tau τ — an educational agent harness that teaches you how to build agent harnesses
i will be publishing tutorials and demos on how to use it to create your own TUIs, harnesses, extensions, etc.
Happy Tau Day!! 🤓
👉 https://t.co/5sWxNtXTZP https://t.co/uiViXChBbw
what could've been, anon 🥲 https://t.co/y6oOFIRVjV https://t.co/LBphPjHDAD
I’ve been tinkering with some new skills for my skills repo over the weekend, and it seems like there’s so much untapped potential for design engineering skills.
/emil-design-eng skill can already help you a ton, and it’s doing more than 100k installs already, but there’s more to come soon.
I plan to add more fine-tuned skills for specific use cases, but since AI produces non-deterministic results, there’s still quite a bit of testing to do as I want the answers to be precise and correct.
In the meantime, you can try the two existing skills:
https://t.co/BQ3QdOdPya
New in-progress skill: /loop-me
Interviews you about your work and finds opportunities for delegating your day-to-day work to AI
https://t.co/B80srzl3cx https://t.co/stfaXS8ZF8
ast-grep outline is released!
It gives you a fast local map of code structure, between grep and a full language server.
Syntax aware, no index to maintain, and extensible through declarative extraction rules. https://t.co/WD6S9cbipV
This is what happens when you plug LLMs into voice assistants, instead of a decade of handwritten rules.
This video dissects Voxtral (a family of OSS speech models) and the foundational work behind it (audio tokenization, semantic/acoustic disaggregation, etc).
Thank you @MistralAI for your collaboration and for your detailed technical reports in an increasingly opaque industry!
00:00 Intro
01:03 Modular vs end-to-end speech models
03:30 Speech-to-Text
06:07 Delayed Streams Modeling (DSM)
09:41 Whisper Streaming
10:33 Voxtral Realtime
13:07 Voxtral Text-to-Speech
14:28 Throwback: WaveNet
15:24 Audio tokenization
20:39 The Voxtral Codec
21:49 Back to Voxtral TTS
25:30 Outro