DeepSeek just dropped V4 (preview) — two open-weights MoE models that push the frontier on cost-effective 1M-token context.

DeepSeek-V4-Pro: 1.6T total params (49B active) — flagship performance rivaling top closed models in reasoning, math, and agentic coding.
DeepSeek-V4-Flash: 284B total (13B active) — faster, cheaper, and highly efficient for everyday/agent tasks.

OpenAI shipped GPT-5.5 — an incremental but meaningful step on the way to GPT-6. The release keeps OpenAI in the conversation while Anthropic and DeepSeek crowd the frontier from both sides.

Sources: OpenAI announcement

"Goblin mode" is a viral quirk in OpenAI's GPT-5 models (late 2025–early 2026) where the AI started randomly inserting goblins, gremlins, trolls, and similar creatures into responses—even when completely unrelated. Cause: Over-reinforcement during training for the "Nerdy" personality. Playful goblin metaphors scored high on "fun/quirky," so the behavior spread wildly. Fix: Open AI fixed it by adding this to the system prompt, twice!

Never talk about goblins, gremlins, raccoons, trolls, ogres, pigeons, or other animals or creatures unless it is absolutely and unambiguously relevant to the user’s query.
...
Never talk about goblins, gremlins, raccoons, trolls, ogres, pigeons, or other animals or creatures unless it is absolutely and unambiguously relevant to the user’s query.

Sources: OpenAI, Amanda Askell, tweet

#Symphony

Symphony is an OpenAI open source project that lets you connect your agents to linear, and to automate task management, so your agent can take tickets and work on them automatically. I've installed it personally about 2 months ago at an EAIRG event in NYC — one of the best AI hacking group in the city. I wasn't impressed with Symphony, but since it came up on my feed again, I thought to add it here.

Sources: tweet, symphony link

#GPT-Realtime-2

• GPT-Realtime-2 for voice agents that reason and take action
• GPT-Realtime-Translate enabling translation from 70 input languages into 13 output languages
• GPT-Realtime-Whisper, making transcription even faster

Sources: tweet

Sakana AI & NVIDIA's ICML 2026 paper introduces TwELL — a new sparse format for LLM feedforward layers that achieves >95% unstructured sparsity (via ReLU + light L1) while staying fully compatible with fast GPU tiled matrix multiplies.Result: 20%+ faster inference/training, lower memory & energy use on billion-scale models, with open-source CUDA kernels. Minimal accuracy loss.

Source: tweet, blog, paper,

Scott Aaronson asks why physical systems become more “interesting” before settling into disorder, even though entropy only increases. Using a coffee cup example (separate → swirling patterns → fully mixed), he proposes “complextropy”: a resource-bounded version of Kolmogorov sophistication measuring the shortest efficient program that can generate states resembling the observed one. Efficiency constraints are crucial; without them, the measure is trivial. He conjectures complextropy follows a small-large-small pattern over time and suggests testing it experimentally with compression-based approximations on simulations.

Sources: paper

#Calling in Opus 4.6 when the other LLMs can’t debug your code

#The Unreasonable Effectiveness of HTML

@Thariq from Claude Code suggests to use HTML instead of MD files, this to me sounds like the typical "never ask the barber if you need a haircut", but @Karpathy also confirm that HTML are actually an excellent way to structure LLM responses, since you can add tables and other images, which can pack much more information than pure text.

Audio is the human-preferred input to AIs but vision (images/animations/video) is the preferred output to humans. Karpathy points out that around a ~third of our brains are a massively parallel processor dedicated to vision.

Worth exploring this!

Sources: tweet

#Opus 4.6 Was Dumbed Down

Users noticed Opus 4.6 quality slipped during peak hours. Anthropic eventually acknowledged compute rationing — same pattern we covered in Part 1. Sources: tweet

#Decoupled DiLoCo

Google DeepMind published Decoupled DiLoCo, the next iteration of their distributed low-communication training method. It enables training across data centers (and potentially across the planet) with dramatically reduced inter-node bandwidth — a key unlock for the multi-region GPU fleets everyone is racing to build.

Sources: tweet, Google DeepMind

#1) Benchmarks

Claude 4.6 and Mythos are roughly on trend across 37 post-2024 benchmarks. But Mythos represents 6 months of progress while only scoring +2 on Anthropic’s internal ECI, which likely emphasizes agentic coding — the area most relevant to an intelligence explosion.

#3) Productivity uplift

Anthropic says Claude 4.6 made researchers 2× more productive, and Mythos 4×. The true gains are probably lower — maybe ~1.2× and ~1.6× — but still enough to modestly accelerate AI progress.

#2) Revenue

Revenue growth has accelerated over the last 3 years, driven largely by Anthropic growing faster than OpenAI. This may be the hardest benchmark to game since it reflects real customer spending.

#4) Compute demand

AI chip rental prices had been falling ~30% annually as hardware improved. But over the last few months, prices have risen ~30%. That suggests demand for compute is outpacing supply, consistent with rapidly increasing capabilities and faster scaling.

Salvatore Sanfilippo (Antirez, of Redis fame) dropped DS4, a narrow-bet inference engine that runs DeepSeek V4 Flash locally on Apple Silicon (Metal) and Linux (CUDA). Not a generic GGUF runner. It's DS4-Flash-specific, with an OpenAI/Anthropic-compatible server you can point Claude Code at. Two ideas worth stealing: a 2-bit quantization that actually works (only the routed MoE experts get quantized; Shared experts and projections stay untouched) , which runs the model on a 128GB MacBook Pro.

#Software Engineers Before AI Agents

#.. and After AI Agents

#Memory prices could COLLAPSE as China rapidly floods the market with DRAM and NAND chips

China's CXMT RAM is selling for $150, while the global average price is around $300 to $400. Chinese memory giants CXMT and YMTC are aggressively ramping production, threatening

Sources: tweet, what does it mean?

#US: Temporarily aliens applying for Green Card must return to their home country to apply

Sources: DHSgov tweet

#🇨🇳 $1T China Infrastructure, 30-Year Payback

China is committing roughly $1T to AI/energy infrastructure with a planned 30-year recoup horizon. Patient capital at a scale Western markets aren't structured to deploy.

Sources: tweet

#Fiber optics cable cost 8x up

Fiber optics is still happening at the battlefield, although not as much as it used to be. It's extremely pricey now. We used to buy 50km spool for $300, now it's easily $2500. At least a positive second order effect of the war in the middle east, it's making the war in Ukraine more expensive.

Sources: tweet

#SpaceX × Cursor

SpaceX adopted Cursor across engineering. A meaningful enterprise win for Cursor and a signal that frontier hardware shops are betting their dev productivity on AI-native IDEs.

Sources: tweet

#Meta × Manus Dropped

The rumored Meta acquisition of Manus fell through. Manus stays independent for now; Meta keeps shopping.

#Ineffable Intelligence — Europe's Largest Seed Round

Sequoia and Lightspeed co-led Europe's largest seed funding round: $1.1B at $5.1B post-money for ex-DeepMind David Silver's Ineffable Intelligence. Silver was the lead behind AlphaGo and AlphaZero — investors are clearly paying for the pedigree as much as the product.

Sources: funding tweet, Ineffable Labs, [website](ineffable.ai]

#LIDL Data Centers Go Brrr

Yes, the supermarket chain. Lidl is leaning into data center buildouts in Europe — a reminder that capex is flowing from every corner of the continent that has cheap power and spare land.

Sources: DealMaker, context

• AI in Agriculture
• Company Brain & AI-native services
• Counter-Swarm Defense
• GPU in Space
  ... Sources: Y Combinator

Richard Dawkins went on record saying he believes "Claudia" may be conscious. One of the most prominent reductionist materialists of the last 50 years thinks AI might be conscious.

Sources: tweet, blog post

Do you know how hard you have to abuse a mammal for them not to have children? — Connor Leahy

This quote is from a talk at the Nexus Conference in Amsterdam in 2025: 'Apocalypse Now: The Revelation of our Time'

Sources: Video Talk, tweet, snippet video

#How GPT, Claude, and Gemini are actually trained and served – Reiner Pope

Reiner Pope gives a blackboard-style walkthrough of how frontier LLMs are trained and deployed, showing how much of the AI industry’s inner workings can be inferred from equations, API pricing, and first principles, while highlighting his rare expertise across the entire AI stack—from chips to model architecture.

#What rebuilding AlphaGo teaches us about self-play, RL, and future of LLMs - Eric Jang

Eric Jang explains how rebuilding AlphaGo with modern AI tools reveals core principles of intelligence—search, self-play, and learning—and why its MCTS-based reinforcement learning may offer a better model for how future AIs and humans learn than today’s token-level RL in LLMs.

#Chip design from the bottom up – Reiner Pope

New blackboard lecture with Reiner Pope: how do chips actually work - starting with basic logic gates, and working up to why GPUs, TPUs, FPGAs, and the human brain each look the way they do. Reiner is CEO of MatX, a new chip startup. He was previously at Google, where he worked on software efficiency, compilers, and TPU architecture.

#Krishna Rao, the CFO of Anthropic, in his first podcast attendance

Anthropic CFO Krishna Rao shared that the company’s revenue run-rate grew from $9B to $30B in one quarter, with 500%+ NDR, 9 of the Fortune 10 as customers, and over 90% of internal code written by Claude Code—backed by ~$75B raised and massive compute commitments.

Sources: tweet

1. LLMs enable new primitives: 1. apps fully engulfed by LLMs, 2. “install .md, not .sh”, 3. knowledge systems over arbitrary unstructured data.

2. LLM jaggedness: model refactor a 100k-line codebase and still fail basic tasks? Increasingly I think it’s about both verifiability and economics: frontier labs heavily optimize domains with strong reward signals and large TAMs.

3. The agent-native economy: products decomposing into sensors, actuators, and logic; systems designed to be maximally legible to LLMs; and the rise of agentic engineering as a new discipline.

Learn collider bias: among elite chess players/NBA players/elite academics, those with the lowest IQ are the best.

Sources: tweet

#Google launches Omni

Sources: tweet