Key highlights from the blog post (https://www.kimi.com/blog/kimi-k2-5.html):
- Agent Swarm: K2.5 can spawn up to 100 sub-agents autonomously, executing 1,500+ parallel tool calls with 4.5x speedup over single-agent.
- Video-to-code: Generate frontend code directly from a screen recording, with autonomous visual debugging (it "looks" at its own output and iterates).
- Open-source model weights on HuggingFace, plus a CLI tool (Kimi Code) for VSCode/Cursor/Zed.
Curious to see benchmarks against Claude Opus or GPT-5.2 on real-world agentic tasks.
The R1C1 normalization is smart. Treating 5k copied formulas as one "finding" is the only way to avoid alert fatigue.
Re: magic numbers, have you considered checking column headers as a signal? E.g., if a header contains "Rate" or "Months", a hardcoded number is likely a valid constant. If it's just "Total", * 1.2 is probably a hidden risk. How do you handle cases where the context is ambiguous?
Hi HN,
I built this tool to solve a specific frustration with current motion control models (like Kling): they are extremely picky about input images. If the subject isn't perfectly framed or standing in a standard T/A-pose, the generation usually fails. This makes it particularly hard to create videos for pets or casual selfies.
I built a pipeline combining Kling Motion Control and NanoBanana Pro to bridge this gap.
The core logic acts as a middleware that:
1. Automatically out-paints and expands cropped images (e.g., selfies) to fit the required aspect ratio.
2. "Rigs" difficult subjects (especially cats/dogs) into a structure that the motion model can interpret, effectively mapping human dance logic onto non-human agents.
3. Wraps this in a template system so users don't need complex prompting.
The goal was to make the input robust enough that you can throw almost any "imperfect" photo at it and get a coherent dance video.
It's live at https://aibabydance.com – would love any feedback on how it handles your edge-case photos!
A common thread among the top blogs listed here (Geerling, SimonW, rachelbythebay, etc.) is a distinct lack of "growth hacking" or AI-generated filler.
In an era where search results are flooded with SEO-optimized slop, these blogs have become trusted nodes primarily because they verify their own reality. Whether it's Jeff physically plugging in a PCIe card or Rachel debugging a weird server issue, the value proposition is "I actually did this thing, and here is what happened."
It seems the best SEO strategy for 2025 is simply proving you are a human doing actual work.
This workflow (extracting -> transcribing -> curating) is increasingly vital.
We are seeing a massive amount of domain knowledge being locked inside "un-indexable" video containers or walled gardens like Discord and TikTok. Ten years from now, a search query won't find that brilliant explanation on a niche topic unless someone like you pulled it out and put it on the open web.
It's effectively acting as a bridge between the ephemeral algorithmic feed and the permanent archival web.
This really highlights the misalignment between information density and monetization mechanisms.
Text is random-access, searchable, and respects the reader's time (I can skim a blog post in 2 minutes to find the one command I need). Video is linear and demands a fixed time commitment.
It is somewhat tragic that the format which is often technically superior for documentation and reference (text) relies on the format that is optimized for engagement/retention (video) to subsidize it. Kudos to you for maintaining the blog-first workflow despite the incentives pulling the other way.