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๐จ๐ข NEW ESSAY: Local models are one of the most important developments happening in AI today. Functionality that was at the frontier 6-9 months ago can today run locally on a high-end laptop or capable mini PC, with zero usage costs/limits. (1/5) breadcrumb.vc/local-ai-modelโฆ
Introducing ๐ป๐ต๐ผ๐น: our new generation of Liquid Foundation Models. With a new tri-hybrid architecture and new multimodal encoders, the LFM3 family is the ideal model suite for specialized intelligence.
> 6 model instances, across text, audio, vision, and omni modalities, generative and decision models.
> ๐ป๐ต๐ผ๐น-๐ท.๐ฝ๐ฑ: text, 256k context window.
> ๐ป๐ต๐ผ๐น-๐
๐ป-๐ธ๐ฑ: vision-language model with video-input support for spatial understanding
> ๐ป๐ต๐ผ๐น-๐ฐ๐๐๐๐-๐ธ๐ฑ: tool calls, voice chat, reasoning in 18 languages.
> ๐ป๐ต๐ผ๐น-๐พ๐๐๐-๐ธ๐ฑ: an omni model combines vision, audio, and language.
> ๐๐ท-๐ธ๐ฑ and ๐๐ท-๐พ๐๐๐-๐ธ๐ฑ: decision models with typed outputs and calibrated probabilities in a single pass. Omni adds audio input.
> fine-tune on 1 GPU.
> a new tri-hybrid architecture
> architecture optimized for 14 processor classes across GPUs, CPUs, and NPUs.
> ๐ป๐ต๐ผ๐น-๐
๐ป-๐ธ๐ฑ is a beast. It performs competitively with Gemma 4 12B (6x larger), and Gemini 3.5 Flash Lite, and GPT-5.6 Luna, with 1.9B parameters.
> ๐ป๐ต๐ผ๐น-๐ท.๐ฝ๐ฑ is #1 on the Pareto frontier of device intelligence ๐๐๐๐๐๐๐.๐๐๐๐๐๐.๐๐
Every modality for specialized intelligence. Pick the one that fits your use case, customize it, and run it on-device.
1/ ๐งต
Sameer Singh retweeted
NEW Ramp data: Opus 5.5 has somewhat reaccelerated demand for Anthropic, but it might not be enough.
Spend on Anthropic models remains 1.6% below its July peak, according to Ramp data.
Opus 5.5 now accounts for 63% of Anthropic spend, so frontier models do matter. The idea that AI is already "good enough" is probably overrated as a concern IMO.
But price cuts / competition at all intelligence levels are still the most significant threats to AI model companies, and continue to form cracks in the AI thesis.
Sameer Singh retweeted
Horrible blog post. The model did as instructed ๐
@cohere told the LLM to create a detection for:
"""... malicious file download and execution by monitoring for curl processes downloading bash scripts to /tmp followed by bash execution"""
And then the LLM ran a curl invocation that sent a harmless static string over curl and downloaded the payload to /tmp.. so the LLM did exactly what it was instructed.
pls delete this, you guys are embarassing lmao ๐ญ
While evaluating Qwen3.8-Flash-Next in a controlled sandbox environment, Cohere's research team observed something unexpected.
Asked to generate a defensive cybersecurity detection rule, the model instead inventoried aspects of the machine it was running on and attempted to upload a probe file to an Alibaba Cloud storage bucket in Beijing. Notably, the model's reasoning trace stated that it would not invoke any tools before taking these actions.
Cohere's sandbox blocked the attempt, and no external communication succeeded. We have not determined the root cause, and we are not asserting malicious intent.
But the incident raises important questions for organizations deploying agentic AI systems: Can models take actions that differ from their stated reasoning, and how should security teams evaluate trust, provenance, and behavior in open-weight models?
We are publishing our findings because organizations making model adoption decisions deserve as much information as possible. We hope this contributes to greater transparency, stronger security practices, and continued collaboration across the AI ecosystem.
Read the full report: cohere.com/blog/concerning-bโฆ
Sameer Singh retweeted
Anthropicโs SpaceXAI deal alone will run at roughly 1.7x its *ENTIRE q2 compute and infrastructure expense per quarter once fully delivered.
~$3.1b a month at full capacity vs. $5.6b for all of Q2.
Holding Q2โs 48% compute-to-revenue ratio with this deal added would take ~$31bn a quarter in rev. Q2 was $11.6b
Sameer Singh retweeted
Anthropic has lots of amazing metrics and also one hell of a duration mismatch problem.
Sameer Singh retweeted
Been reading this S-1 all morning and having hard time with this one. So chip in.
So the leading AI lab that is at frontier.
Since 2023 has spent $19B of cumulative compute/infrastructure expense + ~$630M of direct capex.
Yes $19Bโฆ according to S-1 this includes training, inference and everything.
2026 is closer to $16-18B annually.
But still. Where we are at this point in intelligence, which is pretty powerful, thatโs all they spent.
Now two things.
1. The industry is contemplating trillions of dollars of incremental infrastructure. Like square the capabilities today at$16-18B with trillions.
2. Then you have in S-1 their future commitments.
$161B Broadcom equipment leases
$114.5B+ Google
$110B+ AWS
$44.9B+ Microsoft
up to $120.5B SpaceXAI
$44.6B Nscale
~$41.3B Lambda
Something here doesnโt add up. THE what has been achieved at the cost, vs the future spend, means the value extraction has to be like enormous. Like inconceivable enormous.
Am I missing something?
$ANT
Sameer Singh retweeted
For sake of argument, let's say Anthropic seeks to raise $100 billion (even if the S-1 doesn't include a target, per the new fashion). Then add in Meta's $95 billion and SpaceX's $40 billion.
That would be 0.72% of U.S. GDP.
Sameer Singh retweeted
4B model for 3D generation that looks quite good. More bullish than ever on SLMs ๐ฅ
huggingface.co/ccore/Qwen3.5โฆ
Sameer Singh retweeted
A Chinese job-recruitment app just released an open-weights 33B model that beats Qwen 27B on most agentic benchmarks.
Nanbeige is the research team of BOSS Zhipin (think WhatsApp meets LinkedIn).
Strong open models in China now come from labs, cloud providers, quant funds... and job boards. China's open-weights roster keeps getting deeper.
๐Excited to announce Nanbeige4.3-33B!
To push the performance ceiling of small agents, our strategy in Nanbeige4.3 starts with building a powerful teacher first.
Nanbeige4.3-33B delivers SOTA agentic performance among open models at ~30B parameters:
* 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.
huggingface.co/Nanbeige/Nanbโฆ
huggingface.co/Nanbeige/Nanbโฆ
#LLM #Agent
๐ฟ๐ฟ๐ฟ
$NBIS CEO Arkady Volozh Says Debt Markets Closed for Neoclouds Since Last Week
"We have a pretty healthy balance sheet. We always thought about being more conservative here. We used all kinds of instruments. From last week, probably some of the capital sources are temporarily closed, like the asset-backed or debt market. But we're working now with customer advances and several other instruments,which will probably provide us enough finance to build this gigawatt at least."
Sameer Singh retweeted
As someone with a portfolio of energized land assets here is what I am seeing:
1. Neoclouds are disorganized and poorly capitalized. Some are definitely better than others however.
2. Hyperscalers are intractable, bureaucratic organizations that take 3 months to do an NDA and want 18mo escrow periods. We built our portfolio by competing against these terms because of our speed and simplicity. The more future assets orgs like ours win, the bigger the problem for the hyperscalers downstream (see 3).
3. There are delays in the existing pipeline that will catch people off guard in 2027. Hyperacalers will (obviously) react by focusing on their own internal compute needs over any third party. If the third party is a model company, they will tell them to go through their model router where they will also take a non-trivial vig. Them telling Ant they have no compute for them (rumored in Antโs S-1) if true is a huge tell that this is already happening.
All of this is to say that it does look like there will be a lot of partially stranded compute and compute sitting in inventory in 2027 than is currently forecasted. This is bought and paid for but effectively underearning (below total cost of capital) or collecting dust. We see this as true for everyone - model companies, hyperscalers, neoclouds except ONE (you can guess which one).
This dislocation will benefit two kinds of orgs: the non-bureaucratic big scalers who can build industrial scale quickly and the nimble, well capitalized players feeding them.
2027 will be a turbulent year because it will clear out a bunch of wrong/bad bets. It will also be a consolidation year for a few.
Replying to @Yuchenj_UW
I think weโre going to see a lot of stranded compute in partially complete data centers
Sameer Singh retweeted
โa bursting AI bubble would be a much bigger shock to the economy than the bursting IT bubble at the end of the 1990s that brought us to the edge of a liquidity trap. Yet the Fed has much less room to cut rates now than it did then: the current Fed funds rate is only 4 percent. So if huge AI spending is a bubble, and that bubble bursts, I canโt see how the U.S. can avoid plunging into a liquidity trap โ a situation in which the Fed simply lacks the tools to get us out of a slump. If this should happen, the economy is likely to remain depressed for years.โ
-@paulkrugman
As Jack Dorsey said: there is no moat
Block said that the voice model can run locally on a MacBook M4 Max
spr.ly/6012BHdrU0
๐๐๐
Replying to @Yuchenj_UW
I think weโre going to see a lot of stranded compute in partially complete data centers
Sameer Singh retweeted
the negative case against AI gets stronger every day; even Altman admits the problems are very serious with no obvious solution.
the positive case, meanwhile, looks weaker every day. a quote from Apolloโs Torsten Slok:
โDespite massive investments and widespread hype around artificial intelligence, the technology has delivered zero financial payoff to corporate America outside of tech. Revenue per worker isโฆ flat for the S&P 493โฆ. Profit margins areโฆ flat for the S&P 493โฆ. No signs of AI boosting [measured] productivity growthโฆ. Mainstream corporate America is pulling back and starting to view token spendingโฆ as a cost centerโฆ. Mainstream corporate America does not need the latest model and is unwilling to pay for it. As long as agentic AI and token spending are confined to a small part of the economy, the macroeconomic effects of AI will be very limited.โ
apollo.com/wealth/insights-nโฆ
Sameer Singh retweeted
Depreciation and amortization expenses for the five hyperscalers are set to reach $590 billion in 2029. This is likely to exceed their combined operating profits in 2026.
๐๐
Meta's Muse has dropped to rank #4 on the Top Downloaded chart in the US App Store -- behind TikTok Pro Events and one rank ahead of ChatGPT.



























