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erik

@etash

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erik
@etash
In light of the recent hack by Lazarus, I figured I'd highlight one of the best books I've read on NK. Most media focuses on Pyongyang and NK's propaganda machine but this provides a glimpse into daily life outside the Hermit Kingdom's capital.
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erik
@etash
praying for Huda
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erik
@etash
i dropped in mid-season this show is insane
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erik
@etash
good explainer on the Cluely raise https://x.com/menhguin/status/1936175187084689673
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erik
@etash
everyone in the club is throwing up, crying
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erik
@etash
watching love island with the gf, the edit on this thing is madness
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erik
@etash
first summer in nyc, what’s your advice for surviving the 100F+ napalm strike hellfire days?
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erik
@etash
it’s me v it’s me
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erik
@etash
hello from cbw
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erik
@etash
cool makes sense. agreed on islands, they sound cool but i’ve never wanted to mix and match
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erik
@etash
for devs familiar with astro and svelte. when do you use one over the other? current mental framing has been to use astro whenever i create docs or simple static sites, but sveltekit for most everything else wondering if there's a better right tool, right job consideration here
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erik
@etash
https://open.spotify.com/track/4yf4stMtHLrlI1mp5qF0UM?si=7238ee840d5d45aa
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erik
@etash
that little spin move to fade away mid range jumper that TJ McConnell does makes me say “hell yea” every time
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erik
@etash
i love that there’s a built in called caffeinate
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erik
@etash
this show absolutely tossed me in the spin cycle
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erik
@etash
it’s a loooonngggg game, i haven’t cracked it yet but ive seen others do so
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erik
@etash
post useful content consistently and reply a lot every day for a lot of days and then eventually you catch on
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erik
@etash
after a few days of use, wispr flow has successfully been added to my stack
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erik
@etash
main takeaways from openai's newly published agent guide: teams can start adding agents to their workflows right now keeping design and safety top of mind. for product and engineering teams, agents are highly useful in areas where: - decision making is complex - traditional algorithms fall short - rules are difficult to maintain - unstructured data is a challenge every effective agent requires three components: - the model, which serves as the brain - the tools, or APIs, to take action - the instructions, which provide guidelines and constraints one lesson i've learned is that you don't always need the newest or most advanced model. it's easy to assume the latest release is the best fit, but if your use case is tagging emails or processing documents, a simpler model is often more cost-effective. e.g., Sonnet 4 and Opus 4 are strong for programming but older models still outperform in reasoning or writing tasks. choose the right tool for the job. in terms of architecture, you can use either a single-agent or multi-agent approach. anthropic recently showed that multi-agent systems can outperform single-agent setups on certain tasks. these can be organized with a central manager or as a decentralized peer system. guardrails are essential for managing privacy, security, and reputation risks. multiple layers of protection are needed, such as relevance filters and output validation. it's also important to have a human backup for situations where the system flags an issue.
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erik pfp
erik
@etash
one of the best live performances ever https://open.spotify.com/track/61QOvLPX4kjn3TQ7OJV2dL?si=klmJYtCSROK3UxRCTT6FCg
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