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@every ·
Very little is free in NYC. But if you're a paid Every subscriber, our afterparty for Thesis: 2027 costs exactly $0.
Your agent can work late. You have a party to go to. DJ. Passed bites. Open bar. And more.
🥳 Join us at Pioneer Works in Red Hook, Brooklyn.
🪩 November 5, 2026. 6–9 p.m. ET. 21+.
@petergostev ·
Gemini 4 Carbon, Genie 4, AA index 5, Anthropic IPO, 6.1 Terra & more AI https://x.com/i/broadcasts/1NGarobEZPRJj
@deanwball ·
I occasionally run into fortune 100 firms that have not adopting coding agents *at all.* adoption is still very very early.
@deanwball ·
"Yeah, people talk a lot about RSI as AI that does AI research. There's very little talk about self-replicating data centers, this idea that you can use AI to control robots and run fabs and build turbines or solar panels and build data centers. But I think we have that within grasp."
@hamandcheese ·
If any crash is coming for the stock market its for everything that's still radically mispricing strong AI. There may also be an extreme "end of ZIRP"-like tide-going-out effect on forms of hidden leverage that no longer make sense relative to the IRR of AI capex
@AndrewCurran_ ·
I was going to post some new sama quotes from this interview this morning, but prinz already posted a great summary. I will add that this is the third time I've seen Mr Altman talk about self replicating OpenAI datacenters. The first use case for OpenAI robots appears to be building more OpenAI robots and building more OpenAI datacenters. Mr A also wrote specifically about this on his blog a couple of years ago.
'Yeah, people talk a lot about RSI as AI that does AI research. There's very little talk about self-replicating data centers, this idea that you can use AI to control robots and run fabs and build turbines or solar panels and build data centers. But I think we have that within grasp.'
@RhysSullivan ·
how life with cursor tab felt
@svpino ·
This is going to be massive for companies:
It's a service that sits between any AI software and the tools and model providers it uses.
The idea is to let the company control who uses what from a central location, for example:
• Control model access • Control model budgets • Control tool access • Control guardrails
@hamandcheese ·
Adam's doing the Lord's work
@rileybrown ·
As of today October 29th. This is my current AI Stack.
Mostly comprised of 2 agent tools, and 1 that allows me to centralize my plugins and skills which makes it easier to switch agent platforms.
@hamandcheese ·
This is with only ~50% adoption by the Fortune 500 and with very low spend. Diffusion is still incredibly early.
@hwchase17 ·
browser use is one of the fastest growing agent primitives
get started with it in managed deepagents today!
@wesbos ·
We had really funny names for the flavours but they were nixed by the organizers 😩
@elonmusk ·
Our civilization would be winning wondrously if we are exploring the galaxy with Von Neumann probes by 2100!
Larry Page might be right. He has correctly predicted many things.
@AnthropicAI ·
We’ve expanded our AI for Science program beyond individual teams to support some of the world’s leading scientific foundations, institutions, and research groups. https://www.anthropic.com/news/ai-for-science-expands-foundations-research-institutions
@goodside ·
We’ve known since 2023 AI could not continue to improve due to “model collapse”—training on poisoned internet data generated by AI. This is why LLMs have made no progress since that time, steadily declining on all benchmarks, and are projected to disappear completely by 2030.
@AndrewCurran_ ·
Really interesting work from Sauers.
@biohub ·
400 million people live with a rare disease. Fewer than 5% have an approved therapy. Applications are open for Cycle 4 of Rare As One, funding patient-led organizations working on rare respiratory disorders, rare immune dysregulation disorders, and sarcomas. Apply by 12/15: https://bit.ly/RAO-cycle-4
@elonmusk ·
Interesting
@alexolegimas ·
I joined @GoogleDeepMind to develop a research program on the economics of AGI. Today, together with Andrew Koh (@andrewjkoh) and Shane Legg (@ShaneLegg), we are incredibly excited to release The Economics of Intelligence, the first in a series of projects from our group on how AGI will change the economy.
How do capability improvements transform the economy, and when? In this project, we define thresholds of economic capabilities, e.g., minimal AGI, based on how they transform the economy. We then track how the economy–market structure, employment, and wages–changes during the transition across capability regimes.
We have seen exponential increases in what AI agents can do but the broader economy seems almost remarkably unchanged. Part of the reason is that the solo AI agent is the wrong economic object; economic transformation is an equilibrium outcome where organizations compete. Looking at how intelligence functions within organizations, transformation will depend on when two key capability thresholds are crossed: competence (gains on individual skills, e.g., coding, math) and coordination (gains on ability of agents to cooperate and work together).
We show that what economic transformation looks like depends on whether AI becomes supercompetent, supercooperative, or both—the regime of artificial superintelligence (ASI). Tracking this transition shows why the real visible transformation may come from improvements in coordination—a capability most benchmarks don’t yet track. Past a certain threshold, AI capabilities can break the Coasian ceiling on firm size and lead to an economy that looks drastically different than the one we see today.
Two main takeaways: 1. To understand the path of transformation, we should be tracking agentic coordination more closely. 2. The impact of AI will likely show up in wages before market structure or employment.
We are really excited about this paper. Excited to hear people’s thoughts.
Link: https://institute.deepmind.com/essays/the-economics-of-intelligence/
@simonw ·
Fascinating case study of the sharpest edge of what you can do with agentic engineering in the Fable era - my eyebrows raised at least a dozen times while reading this
@ShaneLegg ·
Two new DeepMind Institute essays:
What is Work For? As AI advances how do people value their jobs, for material needs and beyond?
The Economics of Intelligence When will AI capabilities impact market structure, wages and jobs?
Read the essays here -> https://bit.ly/dmi_home
@ArtificialAnlys ·
Fish Audio Drama 3 is the #1 open weights model on both Artificial Analysis Speech Arenas, and #1 of all models on Pronunciation Robustness
@FishAudio has released Drama 3. We evaluated it across both Speech Arenas and our Pronunciation Robustness benchmark.
Key takeaways:
➤ Text to Speech, Provider Voice: Drama 3 scores an Elo of 1,278 across 1,512 arena appearances, ranking #6 overall and #1 among open weights models, 38 points clear of its own Drama 3 Preview at 1,240 and 50 ahead of BreezeBlue's Breeze TTS 2 at 1,228
➤ Text to Speech, Controlled Voice: Drama 3 scores 1,085 across 1,958 appearances, ranking #12 overall and #1 among open weights models, ahead of Ant Group's Ming-3.0-tts at 1,068 and its own Preview at 1,063
➤ Pronunciation Robustness: Drama 3 scores 94.6% across 2,157 rated spans, ranking #1, ahead of Ming-3.0-tts and Drama 3 Preview, both at 93.2%
➤ Speed: Drama 3 generates 66.6 characters per second, ahead of its own Drama 3 Preview at 60.7 and Murf's Speech Gen 2 at 63.0, and behind ElevenLabs' Eleven v4 at 70.0 and v3 Conversational at 66.8
➤ Pricing: Drama 3 is priced at $15/1M characters. Every model ranked above it on Provider Voice costs more: Gemini 3.8 Flash TTS at $16.5/1M, Qwen-Audio-3.1-TTS-Plus at $19.3/1M, Eleven v4 Turbo at $40/1M, Sonic 3.6 at $49/1M and Eleven v4 at $80/1M
➤ Open weights: released under the Fish Audio Community License.
See more details and listen to samples below ⬇️
@MiaAI_lab ·
Another day, another TensorFold update 🚀
The future is bright for local AI.
@arthurmensch ·
Good progress in the last few weeks ! And now the weights
@arena ·
Real-world results are in: GPT-6.1 Terra (Max) by @OpenAI ranks #8 in Agent Arena across nearly 9K agentic sessions from our global community of users.
With +10.6% net improvement and a median cost per task of $0.62, it has reshaped the Pareto frontier! See its placement in the thread. Its median cost per task is:
- 43% lower than Gemini 4 Argon (High) at #7 ($1.09) - 30% lower than GPT-6.1 Sol (Max) at #6 ($0.89) - 88% lower than GPT-6 Astra (Max) at #5 ($5.16)
By signal, its strengths include:
- #7 in Steerability (+13.3%): whether the model can course-correct when you push back - #3 in Bash Reliability (+13.2%): whether its commands succeed, excluding errors outside its control - #9 in Praise vs Complaint (+16.8%): implicit sentiment in users’ reactions
Congrats again to the @OpenAI team on this release!
@ArtificialAnlys ·
Mistral Large 4 is now open weights, narrowly making it the #1 open weights model developed outside of China
@MistralAI has updated and released the weights of their flagship model, Mistral Large 4, with 1.05T total and 52B active parameters. With high reasoning, Mistral Large 4 scores 26 on the Artificial Analysis Intelligence Index v5, up 1 point from Mistral Large 4 Preview (25), with the major differences being increased token efficiency and reduced hallucinations. It sits 1 point ahead of the leading US-developed open weights model, Muse Spark Open (xhigh, 25).
On the Artificial Analysis Cyber Index, Mistral Large 4 (high) scores 51, third among current open weights models behind DeepSeek V4.1 Flash 1017 (max, 57) and MiMo-V2.6-Pro (56).
Key takeaways:
➤ Narrowly leads non-Chinese open weights models: Mistral Large 4 (high) leads Muse Spark Open (xhigh), the next-best open weights model developed outside China, with the largest gap on Terminal-Bench 4.0 (25% vs. 8%). It trails leading open weights models DeepSeek V4.1 Flash 1017 (max) and GLM-5.3 (max) by 6 points on the Intelligence Index, largely due to weaker coding and agentic knowledge work performance: 15% on FrontierCode 2.0 (vs. 29% and 22%, respectively) and 1349 Elo on AA-Briefcase (vs. 1570 and 1504)
➤ Increased token efficiency: Mistral Large 4 (high) uses 82K output tokens per Intelligence Index task, 39% fewer than Preview (135K), placing it on the open weights Pareto frontier. The seven open weights models that score higher use between 113K and 225K output tokens per task
➤ #2 open weights model on the Coding Agent Index: On the Artificial Analysis Coding Agent Index, Mistral Large 4 (high) scores 48 in Mistral Vibe while using 8.5M tokens per task, about half as many as the leading open weights model GLM-5.3 (max) in Opencode (49, 15.9M). It also scores above another leading open weights model, DeepSeek V4.1 Flash 1017 (max), in DeepSeek Harness (46, 16.7M)
➤ Reduced hallucinations: Mistral Large 4 (high) has a 28% hallucination rate on the updated AA-Omniscience v1.1, down from 41% for Preview and matching Muse Spark Open (xhigh). However, its accuracy remains relatively low at 26%, similar to Ling 3.1 Flash 1019 (27%), and behind DeepSeek V4.1 Flash 1017 (max, 40%) and Muse Spark Open (xhigh, 42%)
➤ Lower price and cost per task than the Preview: Mistral Large 4 is priced at $0.70/$2.10 per 1M input/output tokens, a ~50% reduction from the Preview's pricing of $1.36/$4.18. At the new pricing, it costs $2.06 per Intelligence Index task, down 63% from $5.54 for the Preview. This is comparable to Muse Spark Open (xhigh, $2.02) but more costly than leading open weights models DeepSeek V4.1 Flash 1017 (max, $0.60) and MiMo-V2.6-Pro ($0.26)
Key model details:
➤ Size: 1.05T total parameters / 52B active parameters
➤ Context window: 1M tokens
➤ Multimodality: Text and image input, text output
➤ Pricing: $0.70/$2.10 per 1M input/output tokens ($0.07 per 1M cached input tokens)
➤ License: MEOW License (Mistral Ecosystem Open-Weight License), where operating a Model-as-a-Service business requires a separate commercial license with Mistral
Congratulations to the team at @MistralAI on the release!
@itsPaulAi ·
Mistral is... BACK?!
They've just released what might be the absolute best open weights model for coding
- Better than Kimi/DeepSeek/GLM on terminal bench 4 - 1st on cognition's private frontiercode 2.0 - Fewer tokens per task than others
Already available on Hugging Face and via api!
@ValsAI ·
Mistral Large 4 OSS debuts at #5 among open-weight models on the Vals Index and #17 overall. 🐈
Its accuracy is comparable to Large 4, but it completes tasks with far fewer tokens and steps.
@rowancheung ·
Top 5 learnings growing The Rundown to 3.5M total active subscribers and 55%+ open rate:
1. Email is its own platform, treat it like YouTube, Instagram, Twitter
Super readers (open every email, click ads, and stick around for years) genuinely like email as a medium. We have millions of readers who don’t use social media. Best way to acquire them is through other newsletters, referrals, or being the reason they stop using social media.
Barely anyone is building for this audience. I’m more bullish on email than any other platform (long-form YouTube is a close second).
2. The newsletter industry lacks innovation
Look at mobile apps, ecom, edtech. Custom quiz onboarding, a landing page for every ad, hundreds of new creatives a day. Meanwhile most newsletters, including some of the biggest in the world, have a basic landing page and standard onboarding.
We built an onboarding quiz funnel that, at one point, got us close to negative CAC with hundreds of thousands a month in ad spend. That’s what let us scale aggressively and outbid competitors.
With AI coding and platforms like beehiiv, there’s no excuse not to build new experiences for your readers. The opportunity here is wide open :)
3. Newsletter sales is mostly an education problem
We still run social partnerships at a significantly higher CPM than our newsletters, yet the newsletters convert better and get way more clicks.
A lot of advertisers don’t understand newsletters as they do with influencer marketing, so at scale it becomes an education game.
Once advertisers get it, they stay. We’ve been booked out at multiple ads a day for the last 3 years, months in advance.
4. Stay lean, fast, aggressive
Other newsletters have built large sales and content teams. I think it’s better to stay small and pay high-quality people to do very good work.
Revenue can increase with more employees, but the opportunity cost of not moving fast in the AI era is a long-term net negative.
For example, we constantly test new growth experiments and new forms of media. Most don’t work and that’s fine. The point is we can pivot the same day because we're lean.
5. Lastly, don’t listen to people who say email is dead or that you shouldn’t run paid ads. That’s ignorance and lowkey skill issue.
