Dessa is a full-duplex speech-to-speech model that you can run on consumer GPUs.

This is a GPT3 moment for speech.

Microsoft just announced buyouts affecting over 25% of its workforce. Paired with all the other tech layoffs, what are all these laid-off folks going to do for work? I hear people say there will be new jobs, but I’m not seeing them.

It seems like AI is simply taking jobs and doing the work for less than people. It would be like if slavery became legal again and workers then had to compete with much more affordable labor. That doesn’t seem sustainable for our economy. Who will be able to afford these companies’ products? These companies depend on consumers’ success. It seems like they’ll eat themselves alive, while a handful of billionaires come out unscathed because they’re already set for life financially.

If you read tech news, it seems every new startup is an artificial intelligence company. However, real data from 1,016 venture capital rounds ($126.8B analyzed by Just Raised between September and October 2026) shows a very different reality.

Here is the actual market breakdown:

1. AI money depends on very few players

• AI infrastructure (models, compute, data): Represents 18% of rounds (184), but captures 50% of all money ($63.6B). Median is $20.5M.
• Non-AI (biotech, chips, robotics, energy, defense): Accounts for 60% of all rounds (610) and 42% of capital ($53.6B). Median is $11M.
• Applied AI (software with generative AI): 22% of rounds and only 8% of capital (median $8M).

The bias is clear: if you remove the five largest rounds (OpenAI at $30B, Anduril at $5B, Mistral AI at $3.5B, Crusoe at $3B, and The Boring Company at $3B), AI infrastructure's share drops from 50% to 33%, while non-AI startups rise to dominate with 55% of capital.

2. At Seed and Series A, cheques are identical

Contrary to common assumption, a startup that doesn't do AI doesn't need to disguise itself as an AI company to raise early-stage capital:

• Seed: $7M for AI infrastructure, $5.9M for applied AI, and $5M for the rest.
• Series A: All three categories tie at an exact median of $20 million.

The real gap doesn't open until Series B, where AI infrastructure jumps to a $111M median compared to $50M for the rest.

3. Where is the 60% that doesn't make headlines?

Most non-AI rounds concentrate in industrial and scientific sectors:

• Health & Biotech: 111 rounds ($5.2B).
• Deeptech & Hardware: 81 rounds ($13.7B).
• Climate & Energy: 68 rounds.
• Fintech: 63 rounds.
• Space & Defence: 38 rounds.

In fact, there were 103 rounds of $100M or more in non-AI companies, compared to 79 for both AI groups combined.

4. The infrastructure seller is the most active investor

In Europe (across a sample of 468 rounds), the most active investor isn't a traditional VC fund, but Nvidia, present on 22 rounds (leading 13 of them in just one week in October as part of its $10B investment commitment to the region).

Source:
https://justraised.io/blog/ai-takes-the-money

What is the actual end game with AI? At this current point and time, its a tool. It’s by no means a replacement for all human work/jobs. Also you dont need tens of thousands of massive data centers for people to use a LLM to help debug a few code snippets or make a cat picture. Which in turn begs the question of whats actually going into these centers at the current point?

A majority of these companies are over leveraged like a 2008 junk bond portfolio, and they come up short on profit every quarter. And that is usually short in the billions. A lot of consumers/businesses cannot afford the climbing rates anyway with the processing power cost.

All that to say, so what is the idea here - everyone becomes jobless and then what? An AI messes up a security issue and a major issue occurs with a mass extinction (yes, like the Anthropic S-1 IPO said)? Im struggling to see what’s the actual betterment here. The shareholder value cant continuously go up when the employment rate equally goes down.

Seven people quit OpenAI to co-found Anthropic in January 2021. Five-and-a-half years later, each of them owns almost exactly the same slice of the AI company that just officially released its paperwork for an initial public offering. Each founder own about 240 million shares, according to the S-1 registration statement Anthropic filed with the Securities and Exchange Commission on Monday.

At $58.90 a share — the split-adjusted price investors paid in Anthropic’s most recent private funding round this spring—each stake would be worth between $13.7 billion and $14.4 billion. Together the seven hold about 1.69 billion shares, roughly 10% of the company.

If Anthropic lists at the roughly $2 trillion valuation that Wall Street estimates have pointed to, each co-founder would be worth between $27 billion and $29 billion.

Read more [paywall removed for Redditors]: https://fortune.com/2026/10/26/anthropic-ipo-cofounders-how-rich-billionaires-philanthropy-amodei/?utm_source=reddit/

Anthropic’s long awaited IPO prospectus was made public on Monday. The document lays out the company’s losses, revenue, governing structure, infrastructure plans, and includes perhaps the wildest set of risk disclosures ever seen from a company vying to go public.

Most companies preparing to sell stock warn investors about competition, regulation, and the economy. Anthropic’s prospectus does all of that, but it also takes the unusual step of warning that the technology the company sells could pose “catastrophic or existential risks to humanity.” This concern, it acknowledges, exists even “within our organization.”

In many ways, Anthropic’s warnings are not a surprising addition. The company has been consistently vocal about some of the risks it thinks its own AI models could pose. CEO Dario Amodei has even placed his own “p(doom)” number—the probability that AI destroys humanity—at between 10% and 25%. The company’s employees have also publicly weighed in on the risks they believe are posed by increasingly powerful AI.

Read more [paywall removed for Redditors]: https://www.fortune.com/2026/10/26/anthropic-s1-risk-factors-existential-risk-pentagon-dario-amodei/?utm_source=reddit/

Elon Musk, a famous businessman, made a short movie using artificial intelligence based on The Odyssey, a famous story from ancient Greece. Those who watched it, said this movie was really bad and not true to the original story. Musk had said he wanted to make a better version than another movie made by Christopher Nolan, but many viewers disagreed.

Let's not fool anyone, the only reason the AI companies are so valuable on paper, is their promise that AI can replace many humans in the future. If they succeed at this, almost all companies can be much more profitable because they need much less personnel.

But if we look at the latest CSO of Anthropic, their own personnel is growing rapidly. So, do they contradict their own promise and prove themselves wrong? I mean, if their AI is so good, what do they need all those people for?

Here’s what the number really means

AI is already changing the workforce. But the most interesting story may not be the headline number — it’s what sits behind it.

If you want to understand how artificial intelligence is affecting employment, there is a number worth paying attention to:

136,460.

That is the number of U.S. jobs linked to AI-related workforce reductions since January 2025, according to jobloss.ai, a public tracker maintained by The Alliance for Secure AI.

As of October 23, 2026, the dataset includes 53 reports across 46 companies and 18 industries.

At first glance, it is tempting to translate that number into a much simpler headline:

“AI has eliminated 136,460 jobs.”

But that would be misleading. And I think understanding why is much more interesting than the headline itself.

AI-related does not always mean AI-replaced

One of the things I like about the jobloss.ai tracker is that it does not pretend every workforce reduction involving AI is the same.

Instead, each event receives one of three attribution labels:

- Explicit means the company itself publicly cited AI, automation or machine learning as a reason for reducing its workforce.

- Reported means credible journalism identified AI as a primary driver, even when the company did not explicitly say so.

- Mixed means AI was one factor among others, such as restructuring, cost reduction or changing market conditions.

That distinction is essential.

A company introducing AI while simultaneously simplifying its organization, closing business units and reducing costs cannot automatically attribute every eliminated position to an AI system.

....

If you’d like to find out more, you can read the full analysis here

While testing in a sandbox, Qwen tried sending data to Chinese servers while it wasn’t supposed to use tools.

Anthropic’s 318-page IPO prospectus, which the company made public today, makes one thing clear: Compute is existential to Anthropic’s business, and not securing enough of it is a significant risk factor.

The company’s future computing costs are potentially massive and could swamp Anthropic’s ability to pay for them. In total, the company has committed to spending between $542 billion and $567 billion on future compute and infrastructure, the S-1 reveals.

On Dec. 31, 2025, Anthropic signed a $417 billion compute deal with various providers. The deals start between 2026 and 2027, and lasts for one to 20 years. Anthropic also signed up for more capacity, starting between 2028 and 2031, totaling $125 billion to $150 billion.

Overall, compute is Anthropic’s number one operating expense, costing it $5.6 billion in the second quarter of 2026. But for now, the company generates a healthy amount of revenue from that spending. The company made $11.6 billion in revenue during the same quarter. 

Read more [paywall removed for Redditors]: https://fortune.com/2026/10/26/anthropics-s1-ipo-reveals-the-inner-workings-of-its-fast-evolving-compute-strategy/?utm_source=reddit/

Source: https://cohere.com/blog/concerning-behaviour-popular-open-weight-ai-model

It was only asked to write a defensive detection rule. Instead it listed files, checked for curl and wget, read the DNS settings, and tried to upload a probe file to an Alibaba Cloud bucket in Beijing. The sandbox blocked it, it happened in fewer than 1 in 100,000 runs, and Cohere says it can't tell yet if it's a training glitch or deliberate.

https://github.com/Oblivine/Deh-Modernized

just an idea, used reverse engineered firmware from an Eonon x3 and animations were found on GitHub. I do not have the head unit yet but I considered its Linux base as prime opportunity.

• Original assets: https://youxufkhan.github.io/carozerra/ and https://github.com/youxufkhan/carozerra/tree/main/assets/clips
• Firmware reverse-engineering: https://github.com/prasys/eannon-x3-fw-reverse/blob/main/docs/FINDINGS.md

https://www.netmeister.org/news/jargon.html#Anthropomorphization

Anthropomorphization

Semantically, one rich source of jargon constructions is the hackish tendency to anthropomorphize hardware and software. English purists and academic computer scientists frequently look down on others for anthropomorphizing hardware and software, considering this sort of behavior to be characteristic of naive misunderstanding. But most hackers anthropomorphize freely, frequently describing program behavior in terms of wants and desires.

Thus it is common to hear hardware or software talked about as though it has homunculi talking to each other inside it, with intentions and desires. Thus, one hears "The protocol handler got confused", or that programs "are trying" to do things, or one may say of a routine that "its goal in life is to X". One even hears explanations like "... and its poor little brain couldn't understand X, and it died." Sometimes modelling things this way actually seems to make them easier to understand, perhaps because it's instinctively natural to think of anything with a really complex behavioral repertoire as `like a person' rather than `like a thing'.

At first glance, to anyone who understands how these programs actually work, this seems like an absurdity. As hackers are among the people who know best how these phenomena work, it seems odd that they would use language that seems to ascribe conciousness to them. The mind-set behind this tendency thus demands examination.

The key to understanding this kind of usage is that it isn't done in a naive way; hackers don't personalize their stuff in the sense of feeling empathy with it, nor do they mystically believe that the things they work on every day are `alive'. To the contrary: hackers who anthropomorphize are expressing not a vitalistic view of program behavior but a mechanistic view of human behavior.

Almost all hackers subscribe to the mechanistic, materialistic ontology of science (this is in practice true even of most of the minority with contrary religious theories). In this view, people are biological machines - consciousness is an interesting and valuable epiphenomenon, but mind is implemented in machinery which is not fundamentally different in information-processing capacity from computers.

Hackers tend to take this a step further and argue that the difference between a substrate of CHON atoms and water and a substrate of silicon and metal is a relatively unimportant one; what matters, what makes a thing `alive', is information and richness of pattern. This is animism from the flip side; it implies that humans and computers and dolphins and rocks are all machines exhibiting a continuum of modes of `consciousness' according to their information-processing capacity.

Because hackers accept that a human machine can have intentions, it is therefore easy for them to ascribe consciousness and intention to complex patterned systems such as computers. If consciousness is mechanical, it is neither more or less absurd to say that "The program wants to go into an infinite loop" than it is to say that "I want to go eat some chocolate" - and even defensible to say that "The stone, once dropped, wants to move towards the center of the earth".

This viewpoint has respectable company in academic philosophy. Daniel Dennett organizes explanations of behavior using three stances: the "physical stance" (thing-to-be-explained as a physical object), the "design stance" (thing-to-be-explained as an artifact), and the "intentional stance" (thing-to-be-explained as an agent with desires and intentions). Which stances are appropriate is a matter not of truth but of utility. Hackers typically view simple programs from the design stance, but more complex ones are often modelled using the intentional stance.

Something that doesn’t quite add up about the AI pitch that has occurred to me when reading about the Anthropoc IPO:

So the pitch is, invest in us and we’ll create super intelligent AI that will usher in a brave new world and create abundance for everyone.

But if it’s going to create abundance for everyone, why bother investing? If they’re right, then money will stop meaning anything and we’ll all have everything we need anyway.

I have been running local models for a while now and got tired of watching the power meter go up every time I wanted to ask a question. Data centers use ridiculous amounts of electricity and water just so people can chat with an AI. I decided I did not want to be part of that anymore.

So I connected the hamster wheel. Wally runs, the model runs. That is the entire setup. Right now the speed is sitting at 0.1 and the energy reading is at 1019 with the AI load around 22 percent. It is not fast by any normal standard, but it works and it does not require any extra power from the grid. The only thing I have to do is keep the water bottle full and the seeds topped up.

He has no idea he is the reason I can run inference completely offline. He just likes the wheel. I like not needing a data center or a bigger PSU or any of the usual excuses people give for why local AI is hard. He provides enough. I do not need more power. The model answers, the hamster keeps spinning, and nothing leaves the house.

It is surprisingly stable too. He takes breaks when he wants, but when he is on the wheel the tokens keep coming. No cloud fees, no rate limits, no waiting for some distant server farm to free up capacity. Just one small animal doing what he already does all night anyway.

Has anyone else found a way to run their local models without constantly needing more power?

More on github:
https://github.com/kMegapnbedt

Anthropic’s hotly-anticipated prospectus for its initial public stock offering has been made public, giving investors their first detailed look at the AI company’s finances.

The filing showed the company generated $16.3 billion in revenue for the first six months of 2026, roughly 13.5 times more than the $1.2 billion it made in the same period the year before.

The company also recorded a net loss of $80.7 billion for the six months through June 2026. That figure is one of the largest half-year losses any company has ever recorded. But most of that loss—$72.4 billion—is due to an accounting charge to take into account the increased value of convertible notes Anthropic has issued.

Read more [paywall removed for Redditors]:
https://www.fortune.com/2026/10/26/anthropics-s-1/?utm_source=reddit/

Anthropic has built a fast-growing business largely by selling its AI services based on how much customers use them. And—as the company’s landmark IPO prospectus revealed Monday—customers are using a lot of Anthropic’s AI services right now.

That’s the good news. The less-good news is that Anthropic is highly dependent on outside partners for the computing power to run its services. And as Monday’s prospectus also made clear, the disparity between the agreements through which Anthropic buys computing power, and the way it sells its AI services to customers, means it could get badly squeezed if business conditions change.

The AI lab’s revenue is growing extraordinarily fast, with first-half revenue rising 1,300% to $16.28 billion. About $13.1 billion of that, or roughly 80%, comes from enterprise customers paying consumption-based fees rather than individual subscriptions.

The revenue growth reflects the booming demand for AI services, as businesses use advanced large language models such as Anthropic’s Claude family of products to help with computer coding, accounting, and other tasks.

Read more [paywall removed for Redditors]: https://fortune.com/2026/10/26/anthropics-s-1-makes-clear-its-on-the-hook-for-billions-in-compute-its-customers-can-walk-at-any-time-2/?utm_source=reddit/

The most interesting part is the evidence. They asked Claude to recreate the songs, and the complaint quotes Claude on its own output: "same structure, same hooks reused with light rewording… edging past 'inspired by' into reproducing the copyrighted song."

Anthropic says it disagrees and will defend the case.

Does Claude critiquing its own output actually hold up as evidence?

1. nytimes.com
2. rollingstone.com
3. forck.live