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100,000% this is why you need to believe in your ideas and actually like them instead of just building what some influencer told you is a "good/trendy business idea" right now
https://t.co/zSf5Z2H78P https://t.co/ryMAyS77qn https://t.co/Gm6gdHaLgp On a mission to inspire 1B people to build stuff!
the reason i love claude code is because it takes forever to complete tasks i come back and it actually did it first try i can do whatever i want while i wait
like open another claude code instance and do it 10x or watch anime or scroll twitter lmao
Re: AGI, I think LLMs are like the discovery of electricity. We still need invent boolean algebra and logic gates, and then discover semiconductors and so on.
Author. Coder. CTO. θηριομάχης. Building: https://t.co/otXT4Wy6WR. Writing: https://t.co/dBPBtyCIHw.
RT @ky__zo: Poland 🇵🇱 is quietly living in 2030 with cashierless shops 💳 scan your card 🚶door opens, 🥤grab what you want 👍 walk out i don…
🇪🇺https://t.co/NdorAWqJC3 @euacc 📸https://t.co/lAyoqmSBRX $118K/m 🏡https://t.co/1oqUgfD6CZ $36K/m 🛰https://t.co/ZHSvI2wjyW $43K/m 🌍https://t.co/UXK5AFqCaQ $15K/m 👙https://t.co/RyXpqGuFM3 $14K/m 💾https://t.co/M1hEUBAynC $6K/m
RT @sanskxr02: As part of our pilot, we enriched 1 hour of egocentric factory footage from this dataset - extracting hands, objects, action…
Co-founder & CEO @HuggingFace 🤗, the open and collaborative platform for AI builders
everyone’s got a fave slide from a State of ___ report, this one’s mine! Observability —> Evals —> Improvement basically: 1. Observability: we wanna make our agents better…so we gotta know what they’re actually doing. Log everything is a good default, if it’s not useful delete it later but at least you CAN inspect it 2. Evals: we have Tasks we care about, we should test our agent on them and extend them as we get more feedback 3. Improvement: We have these magic machines that can process tons of data and pattern match what’s happening, we should use them on our data to figure out how to improve our prompts, skills, subagents… my mission (should i choose to accept it which i already have): get that 29.4% to 0 i used to be (and still am to some extent) heavily “vibes based” for coding and making agents better. like just use it and see the vibes and try stuff but the pattern of analyzing agent traces with an agent to improve an agent is also good…it’s actually great “look at your data” - @HamelHusain “don’t forget to store your data so you can look at it” - me also lots of fun nuggets in the rest of this report like what ppl actually use agents for
agents, harnesses, and evals @LangChainAI, prev @awscloud, phd cs @ temple