brotchie @brotchie
swe @google applied ai . 🇦🇺🌁 github.com/brotchie San Francisco, CA Joined May 2008-
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@alexandr_wang Video model human faces have successfully crossed the uncanny valley 🚀
@Trowa_ @isitwokeornot The emotion expressed on the faces is unreal.
@Grummz Why are there tears in my eyes, woah.
@tferriss @kevinrose I have this weird feeling that Kevin was AI generated, it would kinda be a random show thing to do this and then reveal later 🤣 Kevin MCP. It could be new studio / camera / lighting / editing, But Kevin-in-motion looks kinda 95% Kevin rather than 100% Kevin.
You don’t even need AI for this. A decent data feed of aircraft position data, arrival and departure times and you could run a MIP solver to solve for optimal gate allocation, etc. May be a layer of AI decision making on top, and you could have an AI build it in a loop, shadowing whatever system is currently used with a gradual cut over 🤷♂️
@creatine_cycle “Recursive self improving agent harness, you say? … Never heard of him.”
Opus 5.5 looping for 12 hours would beg to differ. The first pass of decomp to C isn’t great. But you get Opus to setup a TAS harness, run a 100% completion TAS, capture traces. It’s then really good at reverse engineering well named functions, structs, variable names, converts switches to if-statement all while retaining a byte identical binary 🤷♂️
The divide comes down to whether you treat AI like a search engine or like programming. If you ask it a question, it gets it wrong, and you give up, you stay in the 75%. Levelling up happens when you want to do something genuinely hard and refuse to do it manually. You hit a wall, think "okay, let me try wiring up a 10-agent swarm for this," and 100 turns and 12 hours later you finally force it to work. That 12-hour grind is the learning curve. The 25% have been compounding those hours daily for years. Someone showing up today asking "what can AI do?" has a much more brutal upskilling curve ahead of them than they realize.
@four_form "Math is hard for humans, easy for AI"
OpenAI’s math drop nudged me toward “AI is more powerful than I thought” and massively shifted me toward “maybe mathematics isn’t intrinsically that hard: it’s just hard for humans.” The bigger update was about our limitations.
@aquariusacquah "tell me you've never run a 12 hour agentic loop without telling me you've never run a 12 hour agentic loop"
@Rames_Jusso @HyperFrames_ I built this without a framework, not sure I buy the premise of this post. youtu.be/BANjdYePoGI?is…
I gave Claude 5.5 a Suno song and asked for a felt puppet music video for kids. 3 days and 840M tokens later, it explains the Big Bang, fusion, evolution, transistors, LLMs and paperclip maximizers. To toddlers. Smoosh It! 🧶
Claude 5.5 is so good at decompilation that it MUST have been a hard RL task Ant used for post-training. They've found a deep well of verifiable, human-hard engineering tasks. Take ANY binary. Set model N+1 the task to produce C code that byte-replicates the binary.
@Deltaroo3D This is the most “holy shit we’re actually in the singularity” thing I’ve yet seen. That grab, spin, release 🤌
Input is pdfs from kitchen designer with designs, engineering plans, photos of pre-rennovated space (some with scribbled on laser measured dimensions). Photos of marble slabs. Pdfs with quotes of appliances, lighting, hardware, etc. Astra was able to essentially make 3D models in blender from most of the product spec sheets. It looked up resources on cabinet making and was able to construct all of the drawers, drawer boxes. Wood textures are generated using a shader published in a 2025 siggraph paper that simulates tree growth and then cuts slices through it. Modify it by directly talking to Astra or leaving notes on the 3D geometry and asking Astra "address the notes I just left." The entire stack was built "agentic first" so there's a fully featured CLI + MCP for modifying the plans, ensuring constraints are met, ensuring nothing broke after increasing a drawer width 1" (for example). Definitely wasn't one shotted, multiple days of work.
So the trick with Suno is what's in the []. Astra (Extra High here) adds stuff that really takes a song from "meh" to "woah." The biggest leverage thing is when asking for lyrics from Astra indicate that song structure and verse styling be communicated in square brackets. e.g. [Verse 1 | Playful, confident female lead; crisp rhythmic melody; palm-muted guitar and driving bass] Suno's built-in lyric generate a) doesn't do this, so songs lack genre-define structure and all the embellishments that make a good song sound great. Another trick is to remaster Suno outputs using Matchering against a popular song in the genre, makes a huge difference as well.
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