Ken_dev (@CodeToTheMoons) header
Ken_dev (@CodeToTheMoons) profile picture

I run the "Code to the Moon" YouTube channel covering various topics in software development. Rust, programming languages, development tools, and more.

Joined October 2022
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Replying to @wagslane
It seems like IaC helps agents in the same way it helps humans. Serves as documentation, a means of version control, and a statement of intent in a precise, unambiguous language
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Advantages over tui, other than the graphics?
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Rust Atomics are so valuable, but they can be a bit challenging to understand. Here's a full explanation
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Replying to @spacetimedb
Really cool game! Itll be interesting to see if all teams use a very similar strategy, and whether that strategy was devised by the language model or a human
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Replying to @evanyou
Starving oneself may be more palatable than writing code by hand
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Replying to @wvo
so cool!!
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Wow. I still don't understand the economics of companies building these things and releasing the weights, but I'm sure glad they do. Chances of partaking in the Anthropic IPO were already low, and now even lower...
Kimi K3.1 Pro launches at 4th on the Artificial Analysis Intelligence Index scoring 55, making large gains in agentic performance; it will be the leading open-weights model once its weights are published @Kimi_Moonshot's new reasoning model gains 11 points over Kimi K3 (max, 44). It trails only Claude Opus 5.5 (max with fallback, 58), Claude Sonnet 5.5 (max with fallback, 56), and Muse Wonder Full (max, 56) on the Intelligence Index. Once the weights for K3.1 Pro are released it will be the highest-scoring open weights model and the gap between the leading open weights and proprietary models on the Intelligence Index will narrow from 12 points to 3. Key results: ➤ Large gains in agentic performance: Kimi K3.1 Pro scores 3rd on GDPval-AA with 1,846 Elo, up 312 Elo from Kimi K3, behind only Claude Opus 5.5 (1,866 Elo) and Muse Wonder Full (1,857 Elo). Kimi K3.1 Pro also achieves the highest score of any model on Terminal-Bench 4.0. K3.1 Pro scores 67%, up from 13% for Kimi K3 and ahead of Claude Sonnet 5.5 (64%). AutomationBench-AA, which tests agentic SaaS workflows, rises from 58% to 73%. ➤ Gains in other evals are more moderate: Humanity's Last Exam rises from 47% to 52%, CritPt from 23% to 25%, and SciCode from 59% to 61%. AA-Omniscience Index rises from 20 to 30, as the hallucination rate falls from 53% to 44% and accuracy rises from 48% to 51%. ➤ Cost per Task roughly equal to Kimi K3 at $1.98 per Task: K3.1 Pro uses 55K output tokens per Intelligence Index task, 14% more than Kimi K3 at 48K, while its first-party token prices are 33% lower. Additional model details: ➤ Context window: 1M tokens. ➤ Size: 2.8T total, 104B active parameters ➤ Input modalities: Text and image ➤ Weights and license: Weights currently not published, Moonshot is expected to release the weights soon after launch ➤ Pricing: $2/$10/$0.20 per 1M input/output/cache hit tokens, 33% lower than Kimi K3 on all three rates. Congratulations to the team at @Kimi_Moonshot on the release!
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Replying to @ThePrimeagen
I suspect many feel the same way. I think people are going to start asking for more speed and the 16 concurrent agent thing will hopefully become less necessary
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Replying to @theo
agree 💯
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Replying to @zkvoxels
I address this very question in the video
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Replying to @theo
Or the last model they tried was Google Bard
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Replying to @theo
damn, I literally recorded a video recently about how awesome Railway is 😢
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Replying to @ericarthurc
Kind of like the Bun Rust rewrite?
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Replying to @pavelvolok
you're right, these should be examined closely! If it also compiles faster than Rust and generates smaller binaries, that would be awesome
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Replying to @ToadRoaster
And if the previous one was already good, but he’s saying this new one is even better…. 👀
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Replying to @martin_nav_
potentially!
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