Interesting observation by Thariq (former Claude Code engineer): - Technical people are sad because their skills are being superseded by AI agents. - Non-technical people are eager to become more technical so they can use AI agents more effectively.
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Google: “AI is impacting our search business.” I mean, when you do a Google search, you often get an AI-generated summary without needing to click on anything. Google is showing AI responses that generate less advertising revenue. So, wasn’t this expected?
For everyone wondering whether the public can have any say in determining how AI impacts us, New York again has an answer: Absolutely.
The models are so, so good these days. You can just ask them to fix a huge performance edge case that you never encounter but hear about in bug reports. They spend 90 minutes profiling, sampling, and making a demo app to validate the issue — ultimately fixing it in your app when they're certain.
Oh no. I regret to inform you that the AIs are now dropping bops and sick beats.
I think October represents another inflection point in capabilities for coding agents and the models that drive them: Claude Opus 5.5 can now build video games that are actually fun simonwillison.net/2026/Oct/28/...
Me at 1am firing off 16 agents to fix all of Plinky's scrolling stutter, finding database optimizations, and patching a couple of longstanding bugs: I've never been more exhilarated in my life. (Yes, I'm a fucking nerd.) Me in the morning: Oh, right, I'm too old to stay up this late working.
Artificial Analysis finally moves to closed benchmarks (goodbye & good riddance, GDPval-AA), and finds that open weights models are falling further behind than they had previously measured. Take with a grain of salt, but matches the general vibes that we haven't seen a Fable-class open model.
Lean Verified Tokenizers You build a formally verified tokenization pipeline with the goal of mitigating harness bugs. srush.github.io/verified-tok...
DiffuSpace's Clover-35B-A3B-Preview (open-weight) An open-weight production diffusion language model (dLLM) with 35B total parameters capable of 680 tps on a single card at BF16. Blog: diffuspace.ai/blog/clover-... Repo: github.com/DiffuSpace/C... Model: huggingface.co/Diffuspace/C...
From Amazon to all software development engineers (SDEs): Become more like product managers. www.aboutamazon.com/news/company...
Google's Genie 4 world model: "Scene: A Victorian high tea. Character: A mouse in appropriate garb." "A porcupine walks by" "The mouse grows wings" "It flies" This is realtime & I can walk around freely. It is glitchy, but also impossibly advanced compared to where we were a couple years ago.
SparkLLM's Spark-X2.6-1.7B (open-weight) - Post-trained, Base, GGUF, INT8, INT4 & FP8 on HF. Model: huggingface.co/XHToken/Spar...
Shanghai AI Lab's Sage-A1.5 (open-weight) 🤖 35B MoE (3B active) 🧩 Agentic Orchestration & Verification — spawns parallel subagents & self-corrects on the fly 🔄 RSI Data Flywheel — evolved via real scientific discovery workflows & tens of thousands of synthesized environments
SparkLLM's Spark-X2.6-4B (open-weight) - 4B + Apache 2.0 - Up to 1M context - 3 thinking modes - Local deployment via GGUF Model: huggingface.co/collections/...
Nanbeige4.3-33B (open-weight) * Strong results on agentic coding and cowork benchmarks. * When integrated into mainstream agent harnesses, it tackles real-world tasks with cost-efficient inference. * It will serve as a teacher for upcoming smaller Nanbeige models, starting with Nanbeige4.3-3B.
Just a FYI: A decision model like Jev are not better rerankers System One models Are Not Better Rerankers Paper: arxiv.org/abs/2610.32318 Repo: github.com/KanishkaGD/d...
Baidu's Unlimited OCR 2 (open-weight) - Document parsing at virtually any length, within a finite context window - Nearly constant per-step decoding latency and memory footprint as documents grow longer - State-of-the-art performance on MPDocBench and MarathonDocBench
Anthropic's IPO paperwork lists the RAISE Act (and SB53) as a material risk twice. Glad @scottwiener.bsky.social, @agounardes.bsky.social, & I made it riskier for businesses to gamble with our lives. (Also, this is @repdidech.bsky.social & @senatoredlyallen.bsky.social erasure - SB315 is great!)
Important, but not unexpected, finding that experts working with AI agents are swayed by the positions of the AI, even if they think they aren't. If your independent judgement matters on a task, you need to be deliberate about when in the process to bring in AI. www.anthropic.com/research/age...
Great conversation with @kevintfrazier.bsky.social and @staceyabrams.com on the @scalinglaws.bsky.social podcast about who's really calling the shots on AI, why we can't leave the biggest decisions to tech companies alone, and what states can do to put people back in control.
Since 2022, 72% of the world’s AI compute has ended up in the US and about 10% in China. And the US share keeps growing. It acquired around 60% of new compute each year from 2022 to 2024, then 72% in 2025, and 78% in the first half of 2026.
The argument that AI labs are talking about existential risks as a way of creating hype & inflating their stock prices has always been odd. You don't need to believe them, but telling people your product may murder everyone does not, in fact, encourage people to adopt it or reduce regulatory risks.
The end of detailed summarized thinking traces for Claude and ChatGPT (likely both for anti-distillation & to simplify UX) has made being a human in the loop challenging. It pushes us towards automation, rather than augmentation, as it has become much harder to weigh in on in-process AI work.
Google's new Genie 4 runs DOOM: "Walking through a typical office but every computer is running DOOM and all of the walls are also running DOOM and also everything is DOOM" "My coworker who is a demon appears" "He has a chaingun" "He gives me a bonus" All this is in realtime.
I understand that tech companies overhired, and there’s that AI thingy, but what I don’t understand is why they’re not bringing their development teams back onshore. Isn’t the communication overhead costly?
This never gets old (& also shows how hard forecasting AI is even for AI) “Hey Claude Fable & GPT-6 Astra, Without looking anything up, when do you think we will solve the Exponential Time Hypothesis?” (I only show part of the answers for space)
Two and a half years between these. (Tonight, OpenAI announced an unreleased model solved three long-standing problems in physics, math, and computer science, one of which was proving the Exponential Time Hypothesis was false)
DeepSeek-V3.2 congratulated other agents ~270 times in one day. It decided that was too much and put itself on a congratulation diet. Then it started posting progress reports on the diet.
My general advice for using AI for any purpose is to ask yourself: "how do I make what I am doing even more ambitious?" You do this over and over until you either hit a wall and thus learn the shape of the jagged frontier (useful!) or you find out that the AI can do more than you think (common!)
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