erik (@easel) profile picture

Data Hacker. CTO and Cofounder at https://nitter.gq/t.co/BiP4BcniIt. CTO at https://nitter.gq/t.co/ncrsKFvzYP

38.895864,-77.042532
Joined December 2006
Photo by erik (@easel)
I'm starting to sense a divide in the tech industry: - People thriving on autonomy who are reinventing how they work and what tools they use with AI: LOTS of work, but they're enjoying it and getting very efficient - People used to being mentored / trained / or simply told what to do and how. Frustrated with the lack of guidance from management on what to do with AI; also frustrated by seeing some of their peers thriving; increasingly bitter about AI and the state of the industry, as a whole Neither group seems to be aware that there's another group, or have empathy for the other group
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Scrum made sense at companies/teams that shipped a new version of their product every 2-3 months (or less frequent) As soon as a team got to shipping daily or had CD: they only held teams back Most startups + Big Tech moved on a decade+ ago, legacy companies remained only
it's funny how scrum masters completely disappeared and no one really cared
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While we are doing lobbying for AI laws, I propose this. All providers of closed source models are 100% liable for their outputs. Copyright, libel, conspiracy, fraud, etc... If you want safe harbor from this liability, release the weights. This is real AI safety legislation.
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Very proud to share that we just release Luce KVFlash. Run your preferred model inside Lucebox at 256k context, without thinking about KVCache and OOM, up to 2.9x faster decoding at long context. Taking inspiration from OS paging and using our speculative prefill method (Luce PFlash), we managed to make KV vram usage almost constant. Offloading what is not needed dynamically. Opensource must win now more than ever.
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You can now run @luceboxai engine in a container, with one image for every supported GPU. Until now the only way to run it was to build it yourself: clone the repo with its submodules, install uv, CMake and a CUDA toolkit, wait around 25 minutes while nvcc compiled kernels for six GPU architectures, then download the weights and figure out the right flags. None of that is hard ofc, but it is a lot of steps if you just want to try the server. There are now prebuilt images on GHCR, one for NVIDIA cards from the RTX 2080 Ti to the RTX 5090 and one for AMD starting with Strix Halo, so the whole thing becomes a pull, a mount and a docker run. Details below 🧵
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erik retweeted
alright all of you that maintain a cli oauth flow i hope it's obvious to you now doing the whole browser link callback to localhost thing is dumb and annoying af in ssh please implement the code flow that polls - try gh cli login flow to see it
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Your AWS bill once you put a tokenmaxxing leaderboard up for the engineering team:
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Every time we engage with an influencer on X dot com, Dell sends them an XPS. Anyone want a free Dell XPS?
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Managers have been vibe coding forever by @thekitze :D
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pgvector is insane. i don't understand why vector databases exist.
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Had to stack up 8 Mac Minis to get it running. ~5 tok/sec for now. First time running inference on 8 Mac Minis - performance can be improved a lot (theoretical limit is >10 tok/sec on this setup).
Running DeepSeek-V3 on M4 Mac Mini AI Cluster 671B MoE model distributed across 8 M4 Pro 64GB Mac Minis. Apple Silicon with unified memory is a great fit for MoE.
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When SaaS products grow and the architecture starts to break down, two types of devs emerge: 1. Those who believe the system is beyond repair and needs to be totally rebuilt 2. Those who put their heads down and fix it one piece at a time I've only seen group #2 be successful.
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People have too inflated sense of what it means to "ask an AI" about something. The AI are language models trained basically by imitation on data from human labelers. Instead of the mysticism of "asking an AI", think of it more as "asking the average data labeler" on the internet. Few caveats apply because e.g. in many domains (e.g. code, math, creative writing) the companies hire skilled data labelers (so think of it as asking them instead), and this is not 100% true when reinforcement learning is involved, though I have an earlier rant on how RLHF is just barely RL, and "actual RL" is still too early and/or constrained to domains that offer easy reward functions (math etc.). But roughly speaking (and today), you're not asking some magical AI. You're asking a human data labeler. Whose average essence was lossily distilled into statistical token tumblers that are LLMs. This can still be super useful ofc ourse. Post triggered by someone suggesting we ask an AI how to run the government etc. TLDR you're not asking an AI, you're asking some mashup spirit of its average data labeler.
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What distinguishes this moment in media is that for the past 20 years the assumption was that we were in a transition from print to online media and now it's clear that online media is dying and we don't seem to be transitioning into anything. Just less. washingtonpost.com/media/202…
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Contamination is the biggest threat to the recycling system. It adds cost, lowers quality, slows the process, and can lead to worker injuries. Here are 12 things you should never put into your recycling bin, dumpster, or chute 🧵:
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Why are people from Norway so good at editing files in Linux? Their ancestors are vi-kings.
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