idlebit

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idlebit

idlebit

@idlebit

Working on @DurableRunning and https://t.co/eqrsFWonrC

California, USA Katılım Mart 2021
1.2K Takip Edilen1K Takipçiler
idlebit
idlebit@idlebit·
Does any remember an internet where nobody really expected users to pay for anything ever? Startups shipped free products to win eyeballs, not dollars. Hobbyists might tack a banner ad on their site.
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idlebit
idlebit@idlebit·
@p_millerd Don’t tell them they aren’t allowed to cry. Do tell them they aren’t allowed to scream. Do tell them they aren’t allowed to cry when tv time ends. Etc etc. if they insist on inappropriate crying or screaming, they have to do it alone in their room with the door closed.
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Paul Millerd
Paul Millerd@p_millerd·
anyone have tips for hypersensitive 3 year olds having lots of tantrums and screaming?
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idlebit@idlebit·
The Loom 🧵 — woven from my Yarn Words stash
idlebit tweet media
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idlebit@idlebit·
Never sleep on a domain name. It might be gone before you wake up.
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idlebit@idlebit·
@rlacombe @Hybridathlete I mean, strength means something specific in running too. It is the hill sessions, tempo runs. Not pure speed, not pure endurance either. Very common usage for runners.
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Hybrid Athlete Guy
Hybrid Athlete Guy@Hybridathlete·
No, the term “strength” actually has a very specific meaning: maximal force production. And it has nothing to do with how subjectively “hard” something is (and just because something is shorter in duration than something else does not automatically mean it’s harder). What you are talking about, and for some reason really, really want it to be called strength, could be called muscular endurance, durability, stamina, fatigue resistance, sub maximal force production, etc. All of those fit far, far better than strength.
Romain Lacombe@rlacombe

Eh. I do strength training too, and actually enjoy it. But pumping some weights a few times in an AC’d gym is nowhere near as hard as keeping moving and running up steep slopes in the mountains after 10+ hours when your legs barely respond and your entire body screams at you in pain. If that’s not strength, then the word is meaningless.

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idlebit@idlebit·
@stevemagness Biggest unlock for me was getting myself to be OK with the idea of driving to a run. It opens up so many more options, surfaces, terrain.
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Steve Magness
Steve Magness@stevemagness·
You know you're a runner when your #1 criteria for buying a house was "Can I run from my front door?"
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idlebit@idlebit·
@FracSlap Are you actually seeing benefits fine-tuning for the petroleum engineering domain? my understanding is that fine-tuning works well for changing model behavior but for things like knowledge you are better off using a foundation model with appropriate retrieval or agent harness.
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Collin McLelland 🏴‍☠️
Had a guy at Major oil company tell us we are cheating by training AI models to only be good at petroleum engineering. My brother in christ, you are an oil company, what would you prefer the models to be good at?
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idlebit@idlebit·
@round Man I’ve gone through so many iterations of link-related-text-excerpts in my career. This one is really true one of the best, but it is still pretty shitty. It’s just too much for non-power users. Speaks mainly to the fact that this is a really really hard UX problem.
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Romain Lacombe
Romain Lacombe@rlacombe·
@idlebit @paulnovosad Oh that’s a good question! It would be quite feasible to summarize the transcripts, given a set of questions and trusted sources. That said I like starting from books and literature, as the videos mostly just repackage the original research in an accessible way.
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idlebit retweetledi
Paul Novosad
Paul Novosad@paulnovosad·
I made an AI running/training coach last fall. With its guidance, I smoked my 5km PR. It's 100x better than the stuff on offer from Garmin/Strava and it was the easiest thing in the world. The first pass was just a bunch of markdown files and a Codex instance. Some notes 1/
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idlebit@idlebit·
This is fantastic! It's a good point about model knowledge vs. expert knowledge. I've built a personal AI stack for myself, but more from a rehab/prehab injury-prone perspective. I found that AI models are great at scheduling specific exercise programs for specific physical interventions. But honestly, for bigger picture stuff, my favorite source is YouTube. There are about 20 expert videos for any running-science question you could possible have. I wonder if scraping youtube transcripts would be the killer unlock..
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Romain Lacombe
Romain Lacombe@rlacombe·
@paulnovosad I recommend generating a knowledge base of exercise science literature for the type of events you target. I did something this earlier this year for trail running, which helped me train for a few ultras (including the Broken Arrow 46k this weekend!) -> switchback.run
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idlebit
idlebit@idlebit·
this is great, awesome of you to opensource your stack I've run into many of the same hurdles building my own personal AI coach over the last year (mostly for rehab-prehab, less about run planning). You point about .md vs .html is a good one. My main gripe with "just use chatgpt" is that I actually want a "day plan" interface that I can check-off, add notes, etc. I'm used to a paper training journal, so I need that surface. I have an app that adds UI on top of all this, but pure codex/claude is certainly attractive: per-token pricing is way more expensive than monthly plan pricing 😅
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idlebit@idlebit·
@Rohanburdened Surely you could swallow something that could synthesize protein in your gut.
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idlebit@idlebit·
@DrPhilipSkiba What does it mean? Doing a marathon build now will not make next year’s marathon build easier/more efficient? Ie going from zero to trained is always the same, no matter your past endurance training?
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idlebit
idlebit@idlebit·
@RobHoffman_ Are these all talking head videos or faceless or both? AI or real?
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Rob Hoffman
Rob Hoffman@RobHoffman_·
biggest takeaway if you want to rank and get customers from AI search: YouTube is extremely OP right now I’ve seen AI Search results for highly trafficked bottom of funnel keywords being powered by some of the most hilariously low quality YouTube videos you’ve ever seen
Tim Soulo 🇺🇦@timsoulo

In the last 6 months at @Ahrefs, we analyzed over 1 billion data points across 14 studies. Here's what we learned about AI search optimization: 1) "Best X" blog listicles are the single most prominent content format cited by AI chatbots. They make up 43.8% of all page types cited by ChatGPT specifically. 2) 67% of ChatGPT's top 1,000 citations come from sources marketers can't influence: Wikipedia (29.7%), homepages (23.8%), app stores (6.6%). Only 32.3% are influenceable content like educational pages, reviews, news, and blog posts. 3) 28.3% of ChatGPT's most-cited pages have zero Google organic visibility. These pages get cited repeatedly by ChatGPT despite not ranking in Google at all. A completely separate discovery layer. 4) ChatGPT only cites about 50% of the URLs it retrieves. It fetches dozens of pages per query but uses half as background context without attribution. This means that being retrieved and being cited are very different things. 5) Adding schema markup had zero meaningful impact on AI citations. AI Overviews actually dipped −4.6%, while AI Mode (+2.4%) and ChatGPT (+2.2%) showed changes indistinguishable from zero. 6) YouTube mentions have the highest correlation (0.737) with AI brand visibility out of all the factors we studied (including all the conventional SEO metrics like backlinks, page count, DR, etc). This held true for both Google-owned and OpenAI products. 7) AI Overviews reduce clicks to the #1 result by 58%. That’s up from 34.5% just 10 months earlier. The trend is accelerating. 8) 99.9% of AI Overviews appear on informational intent queries. Transactional, navigational, and local searches are almost entirely AIO-free. Shopping triggers AIOs just 3.2% of the time. 9) For a given search query, Google’s AI Mode and AI Overviews reach the same conclusions 86% of the time — but cite almost entirely different sources (only 13.7% citation overlap). 10) AI Overviews change every 2.15 days on average, with 70% of content differing between consecutive observations. But semantic similarity stays at 0.95. The words, sources, and entities constantly shuffle, but the actual meaning barely moves.

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