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Ethereum
Joined December 2012
One of the most valuable technical skills is explaining complex work clearly.
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lauren
@potetoOct 8
volume does matter look, i get it. if you were an engineer before agents came along, you're rightfully skeptical about lines of code and number of PRs being used as a metric for any kind of productivity. and i agree! or at least, i used to. for ease of explanation i will talk about this from the perspective as being an engineer on a large team of at least 50. volume didn't matter before because we focused on impact. you could have high impact even with a few PRs: for example you could make a one line config change and save the company millions of dollars. but before agents, the times where i saw volume come into the conversation were for the outliers. on the negative end you might have someone who is struggling to perform, on the other end you might have someone who was a "coding machine" archetype. a coding machine was someone who could just lock themselves in a room and emerge with a stack of PRs that solve a huge number of problems. so volume did matter, but only at the tail ends of the distribution, and combined with impact. if you've ever worked at a large tech company before, you'll get what i mean. the thing about agents is that big tech co problems are now small-medium co problems as well. how do agents change this calculus? well, everyone now has the ability to become a coding machine. you have incredibly capable frontier models that can write code better than any of us, at a rate much faster than we can. in this world, if you're still producing the same number of PRs as before, why wouldn't you pause to wonder why you're not being more productive? alien intelligence is here, and yet you can't outship a human coding machine? this brings me back to why i keep talking about trust (xitter.cf/poteto/status/21020504…). if you haven't put in the work to trust your agent's output, it is very difficult to scale up your productivity. and yes, this approach does require more tokens, but i think about this in terms of cost per intelligence. before agents, this cost was very high - you needed to hire many software engineers with high 6 figure salaries to do the work. tokens are still expensive today, but are likely to get cheaper over time (xitter.cf/theo/status/2107924138…) factoring in the cost of intelligence. what looks exorbitant today will likely be affordable in 6 moths to a year. a single engineer with agents can do the work of tens if not hundreds of engineers, at a fraction of the cost. the reality that i don't think has hit yet is that job of the software engineer has truly changed. our job is not merely to produce software any longer (well tbh it was never only about the code, but bear with me for the sake of the explanation), but the machine that writes the software. this idea of a software factory, or a Michelin kitchen as i like to call it, is still a topic of research. and it's the type of stuff i like to share, not to flex, but to show you what's possible when you put a lot of rigor into using agents at scale
Can someone help me understand how shipping 100s PRs a day makes sense? Why do you need to ship so many PRs in the first place? I might just not be AI-pilled enough to understand the need for it yet, so I’m genuinely trying to understand it.
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Thariq
@trq212Oct 6
working at a higher level of abstraction has always required understanding the lower level ones, I don't think coding agents changes this
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Elon Musk
@elonmuskOct 4
No more AI SI It’s better
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codemilli retweeted
My god this is such a good speech that every SWE needs to hear. You know what? Every person should hear it Keep the happy memories, eyes on the reality, be excited about the future. That’s the best that anyone can do
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codemilli retweeted
GPT 6 Sol vs Opus 5.5 in a nutshell :/
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codemilli retweeted
One week on Arc Mainnet. 624M USDC in circulation. $6.8B USDC transferred. Day one was just the beginning.
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Theo - t3.gg
@theoSep 23
Anthropic has no small models that are worth using right now. OpenAI has no large models that are worth using right now. Google has no models that are worth using right now.
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codemilli retweeted
ios 27 업데이트 했더니 이모티콘 합치는 기능 생긴거 너무 웃김
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codemilli retweeted
It’s a big day for the Mac! Mac mini and Mac Studio provide a huge leap in performance, especially for AI.
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codemilli retweeted
Circle 🤝 @binance Circle and Binance are continuing to build together through a new five-year commercial agreement to expand USDC access across emerging markets. Binance has also made a $100M strategic investment in Circle. circle.com/pressroom/binance…
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codemilli retweeted
bro just dropped Gemini's system prompt
Logan Kilpatrick
@OfficialLoganKSep 21
you are going to fail, so fail while daring greatly
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codemilli retweeted
최근에 예전에 공부했던걸 다시한번 살짝 정리할 기회가 생겨서, 정리한 것을 공유합니다. TF/IDF 와 BM25에 대한 내용입니다. docs.google.com/presentation…
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iPhone 17 Pro vs. 18 Pro internals it’s actually insane how much engineering goes into this, especially when so much gets reworked every year
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Internal components comparison between iPhone Duo & Galaxy Z Fold 8 Image from Reddit
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Cua
@trycuaSep 17
1/ Fast Computer Use is now solved with @typesafeai Jev + Cua Driver. Available in development preview for macOS, Windows, and Linux. We call it jev-use. Draft #3943: github.com/trycua/cua
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update: jev is live on @cloudflare ai gateway 🪅
do i know anyone at @typesafeai? would love to get jev on @cloudflare ai gateway
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codemilli retweeted
Projects have changed not only how I interact with Claude but how I code. I stopped managing sessions. I just send thoughts as they come, Claude splits them into threads, and the project remembers how I work. It's where I do a ton of my coding now. [screenshot: my actual prompts for claude code cli yesterday]
ClaudeDevs
@ClaudeDevsSep 17
Today we're rolling out Projects in Claude Code on desktop and web. A project is one conversation with Claude. It splits the work into threads itself, runs them as parallel cloud sessions, passes context between them, and keeps going when you leave. In beta for select users.
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For anyone curious how Jev works, I made a visual explanation using @claudeai :) This is based on the Qwen2.5-RLCD model which @harshagundal released on @huggingface The idea is to replace autoregressive LLM generation by a single Transformer decoder (of a pre-trained LLM), which processes the context + JSON schema only once. The keys and values of those tokens are cached. Next, for each field of the JSON schema, we: 1. pass its field suffix tokens through the Transformer decoder again (reusing the KV-cache) 2. obtain a final hidden state, which we pass through the language modeling head 3. we obtain scores, also called logits, for all tokens in the vocab of the LLM 4. we only look at the scores of the tokens we care about for the given field, and pass those through a softmax to obtain probabilities which sum to 1 5. we take the token with the highest probability. The benefits of this are that: 1. it's fast (we don't need to generate the JSON schema token by token) 2. it's 100% valid JSON (we don't need to rely on the model to generate a valid schema)
12 million views for a JSON classifier? Yeah, we're in a bubble
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codemilli retweeted
I built a trading bot with Jev! Jev decides if it should "buy" or "sell", given the price feed of an asset pair, and executes real trades. It uses Monad to place the orders on Kuru's on-chain order book in every 300ms block. Demo link → jev-trader.vercel.app/
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