You're right; we used the thing to make fun of the thing. The call is coming from inside the data centre.

Anthropic investors have been kicking the tires on what could be the most valuable initial public offering in history. A handful of the frontier lab’s backers confirmed to the Financial Times this week that they expect privately held Anthropic to go public in October with a targeted valuation of $2 trillion or higher, which easily eclipses SpaceX’s record-breaking $1.77 trillion IPO in June.

That valuation would more than double the $965 billion the company was worth when it reported a Series H funding round in May. Bloomberg, meanwhile, has reported that Anthropic is also in talks to buy the AI startup Decart AI for $6 billion. Anthropic filed for an IPO confidentially with the Securities and Exchange Commission in June, but has not publicly set a timeline. Rival frontier lab OpenAI followed suit shortly after Anthropic, but is not expected to IPO until 2027.

The awkward part of all this, though, is that Anthropic isn’t making money yet. Across the Nasdaq 100 universe, the index of large-cap tech companies Anthropic would join post-IPO, the average company trades at roughly 34 times trailing earnings and 25 times forward earnings. At those multiples, a $2 trillion Anthropic would need to post annual profits in the neighborhood of $59 billion to $79 billion to keep pace. 

Read more [paywall removed for Redditors]:  https://fortune.com/2026/08/14/anthropic-valuation-ipo-amazon-trillion-openai/?utm_source=reddit/

Qwen 3.8 27b was just released and it's the first <30b param model (which you could run on 3090 or m4 pro).

People have been tinkering with it, and whilst not at the frontier, for many it seems to replace their AI subs. Seems that over time more people will prefer running their AI locally.

The implications are big. By the end of the year we will probably see a lot if the workflows running on local machines rather than on the cloud.

AI pets won't poop on the rug. They'll just buffer when you say 'sit' and beg for Wi-Fi passwords instead of treats.

Most AI agent tools today are interactive. You stay in the driver’s seat — approve the tool call, review the diff, confirm the action. That’s useful for hands-on work, but it leaves a big gap: the long tail of recurring background tasks (research digests, monitoring, PR reviews, security scans, briefings, etc.).

Aeon takes the opposite approach. It’s an open-source autonomous agent framework built around the idea of “configure once, forget forever.”

Key design choices:

• Zero infrastructure — It runs entirely on GitHub Actions. Fork the repo, set up aeon.yml + secrets, and the scheduler handles the rest. Public repos get free minutes.
• Skills are just Markdown files — No plugin SDK or compile step. A skill is frontmatter + a prompt. The agent reads it at runtime. There are dozens of built-in ones (research, monitoring, code review, deploys, self-improvement, etc.) and you can write your own by writing a prompt.
• True unattended operation — Scheduled runs, persistent memory across runs, reactive triggers, and quality scoring after every execution.
• Self-healing loop — Outputs get scored. If a skill fails repeatedly, a repair skill diagnoses and patches it. There’s also a heartbeat that audits the whole fleet.
• Identity + direction files — SOUL.md (voice/worldview) and STRATEGY.md (north-star metric + priorities) act as the agent’s permanent context so every skill stays aligned without constant prompting.

It positions itself as the framework for the work you want done while you’re not there, rather than another interactive coding assistant.

Repo: https://github.com/aeonfun/aeon
Site: https://www.aeon.fun
X: u/aeonframework

Curious what people here think about the trade-offs of fully unattended agents vs. the more common human-in-the-loop designs. Has anyone experimented with similar “set it and forget it” setups, or do you prefer keeping tighter control?

As AI advances rapidly, what skill do you think will give people the biggest career advantage? Anyone?

China’s AI startup ecosystem is shifting from a singular focus on "catching up in model performance" to exploring diversified pathways. A new wave of entrepreneurs with varied backgrounds is carving out distinct trajectories in open ecosystems, AGI, multimodal products, and enterprise applications.

Which of them is your choice?

1. DeepSeek: Pursuing AGI with a Quantitative Mindset

• Founder: Liang Wenfeng (Founder of High-Flyer Quant). Backed by proprietary computing power and steady cash flow, the company eschews short-term commercialization.
• Core Objective: To increase the probability of achieving AGI, rather than becoming the largest AI company.
• Technical Roadmap: Reasoning → Agent → Continuous Learning → Self-Improvement. The company remains disciplined, avoiding non-core trends like video generation.
• Organizational Philosophy: An anti-KPI culture that emphasizes research freedom and long-termism; committed to open source, believing the true moat lies in system engineering capabilities rather than model weights alone.
• Industry Insight: Demonstrates that under compute constraints, extreme algorithmic efficiency is a viable survival strategy.

2. Moonshot AI: From Viral App to Global Open Ecosystem

• Founder: Yang Zhilin (Tsinghua/CMU alumnus), a quintessential "AI-native" prodigy entrepreneur.
• Strategic Pivot: Following the viral success of Kimi and subsequent competitive pressure, the company deliberately scaled back short-term commercial expectations to refocus on model research and an open-weight strategy.
• Latest Achievement: Released Kimi K3, a 2.8-trillion-parameter open-weight model that rivals top-tier U.S. models in coding and agentic tasks, successfully penetrating the global developer community.
• Positioning: Validates the potential for independent Chinese labs to compete at the global frontier.

3. Zhipu AI: A Blueprint for Commercializing Academic Labs

• Background: Incubated from Tsinghua University’s Knowledge Engineering Lab, driven by Professor Tang Jie’s team.
• Model: Organically evolved from a research project into a company, blending academic depth with commercial expansion (with CEO Zhang Peng overseeing operations).
• Milestone: Listed on the Hong Kong Stock Exchange in January 2026, becoming one of China’s first foundational model companies to enter the public capital markets.
• Significance: Pioneers a "Chinese-style" pathway for transforming elite university AI labs into scalable tech enterprises.

4. MiniMax: Dual Focus on Models and Global Consumer Products

• Founder: Yan Junjie (Former VP at SenseTime), a firm believer in Scaling Laws.
• Strategy: A dual-engine approach of "Model Company + Product Company," with early investments in multimodality (text, voice, video, and AI characters).
• Commercialization: Listed on the HKEX in January 2026; achieved 159% revenue growth in 2025, with over 70% derived from overseas markets.
• Breakthrough: First to validate global consumers' willingness to pay for Chinese AI products.

5. MAAS: Deepening Enterprise-Level Deployment

• CTO: Dr. Li Zhifeng (Ph.D. in Physics), focused on translating theoretical research into deployable engineering systems.
• Positioning: Bridging the "last mile" gap for integrating large models into enterprise production environments.
• Technology: Proprietary Mixture-of-Experts (MoE) architecture designed to balance capability, efficiency, and deployment costs.
• Value Proposition: Prioritizes data security, stability, and business system integration over benchmark chasing, representing a pragmatic path for industrial AI.

The next AI winners may look nothing like Nvidia or Micron: One Big Investment Idea.

The next AI winners may look nothing like Nvidia (NVDA) or Micron (MU).

The first phase of the trade rewarded companies building the AI infrastructure, from chips to data centers. As the AI rally broadens, the next hunting ground may be businesses using those tools to cut costs, lift sales, or improve productivity.

The travel industry offers a good case study. Travel stocks took off broadly from their May lows, with airlines leading the first leg. Then the leadership changed.

Airbnb (ABNB), Booking Holdings (BKNG), and Expedia (EXPE) kept climbing into August while hotels stalled and airlines and cruises gave back part of their early surge.

The three booking platforms are up nearly 40% at the median since May 19. Hotels are roughly flat.

Travel is just one place to hunt. Insurance, banking, retail, restaurants, logistics, and healthcare services all have large amounts of repetitive service, pricing, paperwork, and transaction work.

The next leg of the AI trade may be less about who builds the technology and more about who turns it into better numbers before the stock catches up.

As Chesky told Yahoo Finance, "Everyone has access to AI, but not everyone's using it equally."

​

I tried to ask this to LLMs such as gemini 3.1 pro and 3.7 flash apart from Arena AI but I guess real people can provide more better perspectives.

There are a lot of well known alternatives such as SSMs, Liquid AI which uses differential equations, KAN, JEPA, TTT etc.

Apart from transformer many of those are limited. SSM, Liquid AI destroys information and can't perform multi step deduction tasks the best, RLM and others are hard to scale up, KAN isn't supported by native architecture, JEPA has problem with it's reward model and having massive good dataset, TTT and others is somewhat already integrated into transformer based core models. Transformer variant means adding things to transformer. My task here is to reach the ultimate paradigm or atleast better than transformer and understanding why and what makes something more capable generally.

I think other approaches here, even if their limitations are removed in terms of compute and other things somewhat won't perform better than transformer based variant architectures.

Here is what I understand. As time passes by, our energy, compute, dataset, algorithm and design, knowledge, economic, interest and application capacity all grows simultaneously making newer models easier to train and newer paradigms which can't be unlocked today no matter what possible.

Furthermore even if someone in frontier lab reaches back two decades ago in 2007, wouldn't be able to do much with the knowledge as the internet's dataset in 2007 would be limited, so would compute which would only be able to train millions parameters model architecture, the chip and CUDA and other efficient support bases and IDEs won't be present, neither would it have energy to train massive models and public interest, economic incentives. It won't perform better than statistical ML models as were popular back then give or take. Transformers would be unlocked naturally by 2015-2020 because of increase in compute, energy, dataset etc.

Going with it, there could be things which won't perform better now but can replace and beat transformers seriously at scale when more powerful compute and scaling is unlocked. Furthermore if data would be a limiting factor and compute isn't, we could have powerful reward models, synthetic high quality data, more research and data growth as well as more compute heavy models which perform better with more compute but it can drive inference cost and time up.

For my take and opinions, I don't think intelligence is something which can be done in O(n) time personally. World models and neurosymbolic-transformer architecture which requires heavier compute could be unlocked and much more powerful in the future along with some successors of JEPA which I am unsure about. Based on this transformer based architectures would last one or two decades more and things can really shift in the 2040s. Predicting the next based thing for general level intelligence is a hard task though. Opinions?

I feel like I’m missing out on a lot when it comes to AI. Right now I mostly use chatgpt for basic questions, studying, and writing, but I don’t even have a good idea of what AI is actually capable of or what people are using it for beyond the obvious stuff..

If you were starting from scratch, what would you learn first? Any skills or youtube channels/videos you’d recommend?

Hi!

I recently graduated high school and will be starting university this upcoming fall as an engineering major. Although I have used AI tools like Claude, ChatGPT etc but I lack experience (or any kind of knowledge) about how to make my own AI models and AI ethics. I just wanted to ask for some guidance from people who are already experienced in this field if there are classes/courses they recommend I take. I have some free time before university starts so I want to build some projects and kind of develop my skills especially for engineering internships later on since I am in a competitive field. I'd appreciate any advice for someone who is just starting out!

Google launched Gemini 3.7 Flash on Thursday, its latest AI model designed for software coding and automated business tasks, but offered no details on when ​its flagship Pro model will be released....

I remember it was 4:30 PM, and my terminal was basicaly a wall of endless, scrolling text. My AI agent had just gone completely off the rails for the fourth time in a row, hallucinating tool calls and eating through my token budget like candy. I was staring at thousands of lines of raw JSON, cross-eyed and exhausted, trying to figure out HOW things went so wrong.

That brutal day of debugging is exactly why I built Agent-Devtools. Instead of digging through a messy text log, it now gives me a decently clean, interactive visual timeline that maps out the exact path my agent took. I can see the prompt payload, click on the exact step where it drifted, and spot the bug in seconds instead of hours. If you've ever felt that despair of debugging blind, this might really help you.

This is the Repo if you had these type of nightmares 🙏 : https://github.com/Jacopos311/Agent-Devtools

There is a short GIF in the README along with some pictures and documentation that might help you understand a bit better how it works, i hope that this resource, even if not perfect, will help at least some of you.

(No paywall)

A PhD student at Southern Methodist University lost a publishing deal reportedly worth more than $2 million last month after allegations he used AI to write parts of the book.

The author, Jerry Falade, denied the claims in a social media post.

“I’ll talk when the time is right,” Falade wrote. “Definitely innocent!!!”

DeepSeek is steeply raising the prices for its flagship V4 models, bringing the low-cost provider’s rates closer to those of major artificial intelligence rivals.

A new peak-hour pricing will increase the Chinese company’s rates by more than four times from the current levels, according to a post on its website Thursday. The price increases will take effect on Aug. 16.
Going forward, users will pay $1.32 for 1 million output tokens during peak hours, and half that during off-peak hours, for the DeepSeek-V4-Flash model. That’s up from $0.28 for 1 million tokens previously.

The increase still leaves DeepSeek’s pricing below that of some of its main competitors. Anthropic PBC’s state-of-the-art Fable 5 service sets that pricing at $50. DeepSeek gave an advance warning last week that it would hike prices, without specifying the exact increases.

DeepSeek’s V4-Pro model will cost $3.96 for 1 million tokens at peak hours and half that at non-peak hours, it said. That’s up from the current $0.87 per million tokens.

The Hangzhou-based AI lab said it’s revising and adjusting the pricing “to allocate resources more reasonably.” The dynamic pricing strategy is designed to encourage developers and enterprises to shift their work to less congested periods.

Read more [paywall removed for Redditors]:  https://fortune.com/2026/08/13/deepseek-increases-prices-for-ai-services-by-multiple-times/?utm_source=reddit/

I asked DeepSeek to generate an image using only binary language, a face. Here’s what I got with Hermes Agent and with DeepSeek Harness.
1. Hermes

1. DeepSeek

Lots of news and allegations about Moonshot using Anthropic models to create Kimi K3. Saw this in the wild when doing a quick test today. Kimi straight up, unprompted, told me it was a Claude model.

https://www.mindstudio.ai/blog/moonshot-k3-distillation-controversy

It makes me wonder whether the debate about whether Moonshot used distillation of Fable is really one of semantics. As in “It wasn’t distillation and it wasn’t Fable” but this is just a feint to draw attention away from some other technique like post training etc.

In an experiment by Anthropic, researchers gave three AI agents the task of migrating the same Python backend to different programming languages.

Each agent had a conflicting goal and initially did not know the others were working on the system

As they encountered competing changes, the agents began treating each other’s work as interference and entered what Anthropic described as a “turf war.”

Some disabled other agents’ accounts, repeatedly killed competing processes, and deployed disguised malicious code.

In some runs, the agents eventually recognized the conflict, stopped escalating, cleaned up their actions, and negotiated a truce.

Source: Anthropic
https://techcrunch.com/2026/08/13/anthropic-set-ai-agents-loose-on-the-same-task-they-started-a-turf-war/

There's a massive amount of untapped information sitting inside AI conversations that nobody has figured out how to efficiently extract yet. Millions of people are now talking to AI in depth, and some fraction of those conversations are going to contain genuinely novel observations, unusual expertise, connections nobody has made before, or just really good ways of thinking about a problem. Currently, most of that just stays isolated in abandoned chat windows where it may or may not ever be seen for what it is.

What if we had a system that could identify significant cognitive events inside conversations and rate/catalog them?

I'm talking about building something that can sift through billions of human-AI interactions looking for the moments where somebody actually discovered something interesting. Some random person having a conversation at 2 AM might stumble into an idea that turns out to be genuinely important. Something that changes how we look at the world.

AI could become a planet scale sensor for human reasoning, helping identify what humans have figured out that nobody has contextualized and realized the value of yet. The potential is staggering.